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Paper Citation Record · LEDGER

Relation Geometry in Semantic Space of Language Models

As of 11 August 2026, this Paper Citation Record lists 100 of 243 outbound references and 0 inbound Pith citation observations for arXiv:2607.26762.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.26762 v1

Coverage vector

measured 100 of 243 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T21:52:46.056544Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 243 outbound references displayed

  • verified exact33
  • verified fuzzy0
  • unresolved67
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3bfe5bf1-22d7-47a6-8973-6489ee7eb190 · outbound

This paper cites No clues good clues: out of context Lexical Relation Classification.

Relation Geometry in Semantic Space of Language Models No clues good clues: out of context Lexical Relation Classification

Reference 1

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verified exact
doi, observed 2026-07-30T21:56:18.690972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.715860Z digest=sha256:f7ed526574608eb592b9ef4053185eb8abb13bcd10807c1a487d111b588ed68d

Observation 15a812a3-8e7b-434b-ab67-ea994a7bf9f9 · outbound

This paper cites Inclusive yet Selective: Supervised Distributional Hypernymy Detection.

Relation Geometry in Semantic Space of Language Models Inclusive yet Selective: Supervised Distributional Hypernymy Detection

Reference 3

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source=arxiv_source observed=2026-07-30T21:52:45.722946Z digest=sha256:44058c73a99522da8a5d622f540e6be00ed8da463b46800fbf026e1de1a2bc6a

Observation d5e18c42-8557-4b6b-b1a6-069f015a6d2f · outbound

This paper cites Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics.

Relation Geometry in Semantic Space of Language Models Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics

Reference 4

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doi, observed 2026-07-30T21:56:18.679389Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 2eee0475-e917-4101-9f45-08d2d6991a40 · outbound

This paper cites Introducing Orthogonal Constraint in Structural Probes.

Relation Geometry in Semantic Space of Language Models Introducing Orthogonal Constraint in Structural Probes

Reference 5

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no resolver link, observed 2026-07-30T21:52:45.729692Z

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source=arxiv_source observed=2026-07-30T21:52:45.729692Z digest=sha256:fdc30a0fc01c52e45189c4b938243702c2a1a67df0e8917672c0211906c35476

Observation d487554e-14cc-4be4-8f4d-46c5c9f07b61 · outbound

This paper cites P ro SA : Assessing and Understanding the Prompt Sensitivity of LLM s.

Relation Geometry in Semantic Space of Language Models P ro SA : Assessing and Understanding the Prompt Sensitivity of LLM s

Reference 6

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no resolver link, observed 2026-07-30T21:52:45.733758Z

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source=arxiv_source observed=2026-07-30T21:52:45.733758Z digest=sha256:54d89c5c6ddd2eb47c9323b13a2f6c004baf90ddce565ed7b241c64020828dc6

Observation 41ed6a8d-b278-48df-b11f-5701dd213668 · outbound

This paper cites What Don ' t RNN Language Models Learn About Filler-Gap Dependencies?.

Relation Geometry in Semantic Space of Language Models What Don ' t RNN Language Models Learn About Filler-Gap Dependencies?

Reference 7

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no resolver link, observed 2026-07-30T21:52:45.737512Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.737512Z digest=sha256:4bc5306374d8d75e6faad297bd84599877a61fc11648a8606e995114d8055262

Observation 20d18e78-d925-4078-ba91-e9827e804983 · outbound

This paper cites Computational Linguistics , author =.

Relation Geometry in Semantic Space of Language Models Computational Linguistics , author =

Reference 8

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no resolver link, observed 2026-07-30T21:52:45.740991Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.740991Z digest=sha256:9ffcd51d66d4b2e8d089f7fa52e4a3a0537a6c75dce2b72cc0041fa378e0d398

Observation 38d41c84-d473-40f7-9003-4600b76f1782 · outbound

This paper cites Frontiers of Computer Science , author =.

Relation Geometry in Semantic Space of Language Models Frontiers of Computer Science , author =

Reference 9

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no resolver link, observed 2026-07-30T21:52:45.744312Z

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source=arxiv_source observed=2026-07-30T21:52:45.744312Z digest=sha256:4a374db842067f6b980d6e7615304204f172a4ebb0f2648bce48742ce3f5bab0

Observation 4722a9b8-accc-40ac-b33b-33d5ef5f1e76 · outbound

This paper cites A Survey on Diffusion Language Models.

Relation Geometry in Semantic Space of Language Models A Survey on Diffusion Language Models

Reference 10

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source=arxiv_source observed=2026-07-30T21:52:45.748087Z digest=sha256:4ffd8f560b475dccab930ac977b93741330e0ba388bd2fa9e3f6f6842776e86b

Observation 46c840c6-addb-488f-a921-8ac04fe48ca7 · outbound

This paper cites Large Language Diffusion Models.

Relation Geometry in Semantic Space of Language Models Large Language Diffusion Models

Reference 11

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no resolver link, observed 2026-07-30T21:52:45.752120Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.752120Z digest=sha256:3f8b631f40862208f279045132a5481d7dbe89941d0eac836e4043fcbc304c1a

Observation 813bf915-2049-4ba4-ac17-9de682c5f24f · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Relation Geometry in Semantic Space of Language Models Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 12

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no resolver link, observed 2026-07-30T21:52:45.755677Z

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source=arxiv_source observed=2026-07-30T21:52:45.755677Z digest=sha256:7f8b587f903faa44caf29405794adc462e47a539941db7744dbd66c03cc1a7d4

Observation 48a07dac-4de6-41fa-b4f2-8e8c134495a4 · outbound

This paper cites Word , author =.

Relation Geometry in Semantic Space of Language Models Word , author =

Reference 13

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source=arxiv_source observed=2026-07-30T21:52:45.758872Z digest=sha256:cb49d59b7f4b0d845783dba3413de52da00e36ff16ad3fb1090b3e9e7b0b4ad3

Observation df7980c0-4570-4b92-a653-d0cf84ede6cb · outbound

This paper cites an unresolved cited work.

Relation Geometry in Semantic Space of Language Models Unresolved cited work

Reference 14

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no resolver link, observed 2026-07-30T21:52:45.762478Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.762478Z digest=sha256:b794285e75d7151bd0f90add5d31bb259a3c454ff254670c6dab1b0725ba2b5d

Observation ee44c565-fdf2-4d4b-a62a-869dc5b43462 · outbound

This paper cites The Vector Grounding Problem , journal =.

Relation Geometry in Semantic Space of Language Models The Vector Grounding Problem , journal =

Reference 15

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no resolver link, observed 2026-07-30T21:52:45.765806Z

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source=arxiv_source observed=2026-07-30T21:52:45.765806Z digest=sha256:7a4f6b14707b2c78a3b82049e69f2768f05bca3014be80056b49e898bbfa9da2

Observation dea8dfc4-d4d8-40e3-ac40-dcd3d3b23573 · outbound

This paper cites Physica D: Nonlinear Phenomena , volume =.

Relation Geometry in Semantic Space of Language Models Physica D: Nonlinear Phenomena , volume =

Reference 16

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no resolver link, observed 2026-07-30T21:52:45.769323Z

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Observation 36455c85-ae68-4d17-9de1-e6b01c79a49d · outbound

This paper cites The Italian Journal of Linguistics , year=.

Relation Geometry in Semantic Space of Language Models The Italian Journal of Linguistics , year=

Reference 17

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Observation 18cf3bf4-892b-4b94-9ddd-adde582ebef5 · outbound

This paper cites Weinberger and Yoav Artzi , title =.

Relation Geometry in Semantic Space of Language Models Weinberger and Yoav Artzi , title =

Reference 18

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no resolver link, observed 2026-07-30T21:52:45.775858Z

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Observation 95b14feb-8257-412f-a073-ba3d79390577 · outbound

This paper cites A Fine-Grained Analysis of BERTS core.

Relation Geometry in Semantic Space of Language Models A Fine-Grained Analysis of BERTS core

Reference 19

Resolution
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no resolver link, observed 2026-07-30T21:52:45.778777Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.778777Z digest=sha256:740f48be08241ac13fc9e014efc79f3c9ab84fb592ebe0f85cb6ce67a5087c4b

Observation 8c04004d-6fb2-4252-8309-1a6944427aec · outbound

This paper cites CTRL: A Conditional Transformer Language Model for Controllable Generation.

Relation Geometry in Semantic Space of Language Models CTRL: A Conditional Transformer Language Model for Controllable Generation

Reference 20

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no resolver link, observed 2026-07-30T21:52:45.781862Z

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source=arxiv_source observed=2026-07-30T21:52:45.781862Z digest=sha256:e17b8bba9e693d5aefab38ad9ee801c5f9f703dfa5741b3349309a6498c46af8

Observation 16fbc176-c6b0-4262-99ed-76819a82dd3c · outbound

This paper cites Neural Word Embedding as Implicit Matrix Factorization , url =.

Relation Geometry in Semantic Space of Language Models Neural Word Embedding as Implicit Matrix Factorization , url =

Reference 21

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no resolver link, observed 2026-07-30T21:52:45.785144Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.785144Z digest=sha256:481978b9f25f24b68ae42f04d97311e7ef5eec750b32e5c03eb66b39e5e5a4d9

Observation 33f6c0ca-ca6a-4792-b377-341a42928f4f · outbound

This paper cites and Furnas, George W.

Relation Geometry in Semantic Space of Language Models and Furnas, George W

Reference 22

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no resolver link, observed 2026-07-30T21:52:45.788220Z

Source-reported events for the cited work

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Observation 9fed09b2-6def-4a94-a261-6be3fed2c6f1 · outbound

This paper cites Attention is All you Need , url =.

Relation Geometry in Semantic Space of Language Models Attention is All you Need , url =

Reference 23

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no resolver link, observed 2026-07-30T21:52:45.791554Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.791554Z digest=sha256:c28c6c10036e6a10a5ab2d7ff8b878e58cb392216e970a709e5793b052ab9f7d

Observation eb510363-8ee1-43c9-9d1a-52c612a69eee · outbound

This paper cites Learning Phrase Representations using RNN Encoder -- Decoder for Statistical Machine Translation.

Relation Geometry in Semantic Space of Language Models Learning Phrase Representations using RNN Encoder -- Decoder for Statistical Machine Translation

Reference 24

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no resolver link, observed 2026-07-30T21:52:45.795009Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.795009Z digest=sha256:44dfa58fe57c30d8e264d34686ee8e06b7bd3905bec4d4ce1a273ba382b4f30c

Observation 0d389396-c9bb-473d-a092-7ef423bc8234 · outbound

This paper cites A Neural Probabilistic Language Model , url =.

Relation Geometry in Semantic Space of Language Models A Neural Probabilistic Language Model , url =

Reference 25

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no resolver link, observed 2026-07-30T21:52:45.798303Z

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Observation 251df72b-491b-47cf-8a7b-a23fd93a9ec3 · outbound

This paper cites Discover Computing , author =.

Relation Geometry in Semantic Space of Language Models Discover Computing , author =

Reference 26

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.585498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5139987a-f5a0-4717-9268-c732633eb7e4 · outbound

This paper cites 2024 , pages =.

Relation Geometry in Semantic Space of Language Models 2024 , pages =

Reference 27

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doi, observed 2026-07-30T21:56:18.575759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 41d9e7b7-3047-4c16-9b7f-32d22aa0e0af · outbound

This paper cites Does BERT Know that the IS -A Relation Is Transitive?.

Relation Geometry in Semantic Space of Language Models Does BERT Know that the IS -A Relation Is Transitive?

Reference 28

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no resolver link, observed 2026-07-30T21:52:45.809203Z

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Observation c51de648-722f-4086-8d1d-cfe299fa1390 · outbound

This paper cites Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy.

Relation Geometry in Semantic Space of Language Models Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy

Reference 29

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verified exact
doi, observed 2026-07-30T21:56:18.559022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 4d2e0ee4-4418-43fb-bdc2-b9a7ba7f94c8 · outbound

This paper cites Bridging Perception, Memory, and Inference through Semantic Relations.

Relation Geometry in Semantic Space of Language Models Bridging Perception, Memory, and Inference through Semantic Relations

Reference 30

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verified exact
doi, observed 2026-07-30T21:56:18.548934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.815552Z digest=sha256:9082fbb7f211701fc70890f063e282cc5f141f5d05643beae26cd19aabf61326

Observation 45868611-98ef-41cc-b4e1-62ba003fbca1 · outbound

This paper cites Inspecting the concept knowledge graph encoded by modern language models.

Relation Geometry in Semantic Space of Language Models Inspecting the concept knowledge graph encoded by modern language models

Reference 31

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doi, observed 2026-07-30T21:56:18.538193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.818713Z digest=sha256:597620a4b28037d8c0f6ffe1d5715c5b102c4fc832e7e553af4cfea12936f99e

Observation 53771cb5-53e1-49bd-8448-3ff4b7fbb900 · outbound

This paper cites R eliable E val: A Recipe for Stochastic LLM Evaluation via Method of Moments.

Relation Geometry in Semantic Space of Language Models R eliable E val: A Recipe for Stochastic LLM Evaluation via Method of Moments

Reference 32

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verified exact
doi, observed 2026-07-30T21:56:18.528150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.822218Z digest=sha256:50b0214441a36d68e4565900cdc18095a85eeead1f99ab468dcb83eac8979329

Observation 0f55942a-271c-4155-a439-1298cdd297c7 · outbound

This paper cites How Do LLM s Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training.

Relation Geometry in Semantic Space of Language Models How Do LLM s Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training

Reference 33

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no resolver link, observed 2026-07-30T21:52:45.825519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.825519Z digest=sha256:505aa97ce1ded27610ef0c8fb3c734fdb14a7fd42f0c4d7309f061a10e154738

Observation 429444b7-5e25-4ac5-803d-f6ab18f2b8df · outbound

This paper cites The quasi-semantic competence of LLMs: a case study on the part-whole relation.

Relation Geometry in Semantic Space of Language Models The quasi-semantic competence of LLMs: a case study on the part-whole relation

Reference 34

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verified exact
local_arxiv, observed 2026-07-30T21:56:18.510699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation ef89e7fc-bd95-4f9e-936d-7f8aad3e3d61 · outbound

This paper cites On the Distinctive Co-occurrence Characteristics of Antonymy.

Relation Geometry in Semantic Space of Language Models On the Distinctive Co-occurrence Characteristics of Antonymy

Reference 35

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verified exact
doi, observed 2026-07-30T21:56:18.495998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation f50a1624-b068-4dec-8338-4e67eb97e38a · outbound

This paper cites an unresolved cited work.

Relation Geometry in Semantic Space of Language Models Unresolved cited work

Reference 36

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.835389Z digest=sha256:2d3916d90a831c6ed285f9939e98a15c31e3e4e742a85677dfc6ea36e04e0144

Observation 999a6956-e71b-4eab-8431-11cf690ff87c · outbound

This paper cites Language Resources and Evaluation , year =.

Relation Geometry in Semantic Space of Language Models Language Resources and Evaluation , year =

Reference 37

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no resolver link, observed 2026-07-30T21:52:45.838666Z

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source=arxiv_source observed=2026-07-30T21:52:45.838666Z digest=sha256:2ba2c933446fe4967ee4560e9b35633ee97fd0e1707e4c99ee74ca2af60b34df

Observation 82f9907c-3635-4eca-aa29-66114ff50325 · outbound

This paper cites and Wiersma, William and Jurs, Stephen G.

Relation Geometry in Semantic Space of Language Models and Wiersma, William and Jurs, Stephen G

Reference 38

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source=arxiv_source observed=2026-07-30T21:52:45.842363Z digest=sha256:4b19efc94275abbec0b887de576b95b4f91751610b09c918c9c883eeb4777487

Observation c381f0b0-9987-48c0-b4e0-788092363958 · outbound

This paper cites The Corpus of Contemporary American English (COCA).

Relation Geometry in Semantic Space of Language Models The Corpus of Contemporary American English (COCA)

Reference 39

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no resolver link, observed 2026-07-30T21:52:45.845631Z

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Unavailable: canonical work link unavailable.

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Observation ee8474d6-e045-45e2-89af-7a467f7da27b · outbound

This paper cites Global WordNet Conference 2025 , year =.

Relation Geometry in Semantic Space of Language Models Global WordNet Conference 2025 , year =

Reference 40

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Observation de995b0a-ca9f-4fd6-a712-0f4e4b0f89b6 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

Relation Geometry in Semantic Space of Language Models The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 41

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Observation 88a457cd-8602-4ad4-a200-319468b0cd9b · outbound

This paper cites Scaling Laws for Neural Language Models.

Relation Geometry in Semantic Space of Language Models Scaling Laws for Neural Language Models

Reference 42

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Observation b1dd4a38-a73b-43dd-863a-8481442b252a · outbound

This paper cites and Chaffin, Roger and Herrmann, Douglas.

Relation Geometry in Semantic Space of Language Models and Chaffin, Roger and Herrmann, Douglas

Reference 43

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Observation 2d530cf3-f230-442f-8ded-aa582612277b · outbound

This paper cites and Bousfield, Weston A.

Relation Geometry in Semantic Space of Language Models and Bousfield, Weston A

Reference 44

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Observation 40220943-d33e-4cf3-a893-4878efae2c7e · outbound

This paper cites Where Partonomies and Taxonomies Meet.

Relation Geometry in Semantic Space of Language Models Where Partonomies and Taxonomies Meet

Reference 45

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source=arxiv_source observed=2026-07-30T21:52:45.865460Z digest=sha256:747166f6b7b629e756b796e24e1b4f4343a8ae1f79b5c896493c1470acca7985

Observation 825553d1-032a-4940-9576-0b55843f53f2 · outbound

This paper cites Possessives in English: An Exploration in Cognitive Grammar.

Relation Geometry in Semantic Space of Language Models Possessives in English: An Exploration in Cognitive Grammar

Reference 46

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source=arxiv_source observed=2026-07-30T21:52:45.868533Z digest=sha256:518aad17029818d84af8bc97b867e5f3322bc0718bbece6f4b871b87b8a58263

Observation f291746f-6833-4450-80cd-1b6a585b9048 · outbound

This paper cites Alan Cruse.

Relation Geometry in Semantic Space of Language Models Alan Cruse

Reference 47

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no resolver link, observed 2026-07-30T21:52:45.871980Z

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source=arxiv_source observed=2026-07-30T21:52:45.871980Z digest=sha256:9c477d923ed78abb2091cc3cf6305c9a293ac8c7bd91c0ea3f7c4dddc6b621ad

Observation 5880654e-e686-4a05-a869-e59fcd6c41b2 · outbound

This paper cites Alan Cruse.

Relation Geometry in Semantic Space of Language Models Alan Cruse

Reference 48

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no resolver link, observed 2026-07-30T21:52:45.875265Z

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source=arxiv_source observed=2026-07-30T21:52:45.875265Z digest=sha256:8143f4c587b2c5693977420e8575b40048857319b2c86091f1cf349784b69b2c

Observation d1a63f86-ba8b-4a35-8e78-1b3e3f887558 · outbound

This paper cites Noms collectifs et méronymie.

Relation Geometry in Semantic Space of Language Models Noms collectifs et méronymie

Reference 49

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source=arxiv_source observed=2026-07-30T21:52:45.878600Z digest=sha256:8fe7e0dba4218aeac884073790316d6a3bd95f888fee1fd2ebd3943fdcb2685f

Observation 40cf3b4d-b33b-44d5-9e96-b0819c07107a · outbound

This paper cites Wieso ist ein Kollektivum ein Kollektivum? Zentrum und Peripherieeiner Kategorie am Beispiel des Spanischen.

Relation Geometry in Semantic Space of Language Models Wieso ist ein Kollektivum ein Kollektivum? Zentrum und Peripherieeiner Kategorie am Beispiel des Spanischen

Reference 50

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source=arxiv_source observed=2026-07-30T21:52:45.881867Z digest=sha256:11f5733ca32086c50c847598a483fd966443c9976fe9a966f78f5392419c66e0

Observation df79bef3-dedd-4782-91b8-3378cd320f88 · outbound

This paper cites an unresolved cited work.

Relation Geometry in Semantic Space of Language Models Unresolved cited work

Reference 51

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source=arxiv_source observed=2026-07-30T21:52:45.884826Z digest=sha256:fd2e2eb85728b831bd79c502e163939e646452334cd5ad40536c0457235150de

Observation b0f8cb09-a2d8-4a29-966b-64e7b190d7f0 · outbound

This paper cites Measuring the Reliability of Qualitative Text Analysis Data.

Relation Geometry in Semantic Space of Language Models Measuring the Reliability of Qualitative Text Analysis Data

Reference 52

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doi, observed 2026-07-30T21:56:18.447901Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.888168Z digest=sha256:7b0035623419fb39ede2a88da2259178ce5d9c17bad72585176a33fb2582b3dc

Observation 1b226d49-5fee-4b81-802c-d4c923400734 · outbound

This paper cites Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , pages =.

Relation Geometry in Semantic Space of Language Models Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , pages =

Reference 53

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source=arxiv_source observed=2026-07-30T21:52:45.891564Z digest=sha256:f9dbf656e8081e931ca45f50911a60b97ba41eaed416756e6891cb6b20a4967e

Observation 2f6a4fbc-9d1f-40da-9473-56e437f54122 · outbound

This paper cites Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) , year=.

Relation Geometry in Semantic Space of Language Models Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) , year=

Reference 54

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source=arxiv_source observed=2026-07-30T21:52:45.894807Z digest=sha256:ffdde2299cbf5e2fb3a6ac80dec2b1ef07bed65cb62fc06a9e5e04c67a9accf7

Observation 574aed6a-30b1-4ef9-875a-8b364839bfb8 · outbound

This paper cites Nelson (Winthrop Nelson) and Twaddell, W.

Relation Geometry in Semantic Space of Language Models Nelson (Winthrop Nelson) and Twaddell, W

Reference 55

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no resolver link, observed 2026-07-30T21:52:45.898083Z

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source=arxiv_source observed=2026-07-30T21:52:45.898083Z digest=sha256:95e682154053369dc36a7f776b4b378ff8940ccbd25c2ae77fe586a9ed361b4c

Observation e6fda4b6-7ec1-40b8-a4db-e74200928b43 · outbound

This paper cites Language Models are Few-Shot Learners.

Relation Geometry in Semantic Space of Language Models Language Models are Few-Shot Learners

Reference 56

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no resolver link, observed 2026-07-30T21:52:45.901716Z

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source=arxiv_source observed=2026-07-30T21:52:45.901716Z digest=sha256:985a923af41e603db523ed2aa8c48fabc20d3e8e8bd6dcf9118a601de502e17b

Observation 953282b9-4f16-4f44-969d-685c24701b08 · outbound

This paper cites Probing Classifiers: Promises, Shortcomings, and Advances.

Relation Geometry in Semantic Space of Language Models Probing Classifiers: Promises, Shortcomings, and Advances

Reference 57

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source=arxiv_source observed=2026-07-30T21:52:45.905220Z digest=sha256:454b7349418321523d5b1969202ac47ce16b55323ee3dddcd043543b9a252329

Observation 640ece4d-c4b2-483d-8544-3833f281426e · outbound

This paper cites The Analysis of Synonymy and Antonymy in Discourse Relations: An Interpretable Modeling Approach.

Relation Geometry in Semantic Space of Language Models The Analysis of Synonymy and Antonymy in Discourse Relations: An Interpretable Modeling Approach

Reference 58

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doi, observed 2026-07-30T21:56:18.427295Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.908742Z digest=sha256:200040bc9af2c0643307298bc44801993ef0e080ba89a6d9505e5b606f49250f

Observation c64cdcda-973b-4668-a972-96f93ffde32f · outbound

This paper cites A Semantic Approach to Recognizing Textual Entailment.

Relation Geometry in Semantic Space of Language Models A Semantic Approach to Recognizing Textual Entailment

Reference 59

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no resolver link, observed 2026-07-30T21:52:45.912191Z

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source=arxiv_source observed=2026-07-30T21:52:45.912191Z digest=sha256:4b61a5d2a4efafd01f3fcc146113a5d859a234022fb1a77c3192579751acdce7

Observation 23767c38-d204-4955-b0f3-5b0067ac8d01 · outbound

This paper cites an unresolved cited work.

Relation Geometry in Semantic Space of Language Models Unresolved cited work

Reference 60

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation babb623b-bf98-4b70-b7e5-1c0d0cbd172a · outbound

This paper cites Simplifying Lexical Simplification: Do We Need Simplified Corpora?.

Relation Geometry in Semantic Space of Language Models Simplifying Lexical Simplification: Do We Need Simplified Corpora?

Reference 61

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.918846Z digest=sha256:700be0d70d94c0bc59770d8699c50a1804185d31e9f893df5410aa6e4599b954

Observation b785d4ee-5a68-4b5b-9b9a-6283ec46b84a · outbound

This paper cites Miller and Christiane Fellbaum.

Relation Geometry in Semantic Space of Language Models Miller and Christiane Fellbaum

Reference 62

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 2ff20c03-bc4b-4f6b-abc5-0fd2b6a4d059 · outbound

This paper cites Antonym order in English and Chinese coordinate structures , url =.

Relation Geometry in Semantic Space of Language Models Antonym order in English and Chinese coordinate structures , url =

Reference 63

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source=arxiv_source observed=2026-07-30T21:52:45.925725Z digest=sha256:45d2b8cc9f8cf5c816b39e1e9850113ea0f0143909bf5a3557be0f9877044c93

Observation d5c5f357-501b-45f6-aee9-335cb10410f1 · outbound

This paper cites Charles and George A.

Relation Geometry in Semantic Space of Language Models Charles and George A

Reference 64

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5e2e3914-eac9-4e48-8520-93baa321ab10 · outbound

This paper cites Co-Occurrence and Antonymy.

Relation Geometry in Semantic Space of Language Models Co-Occurrence and Antonymy

Reference 65

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.933494Z digest=sha256:6ae64781d979c5dfed04f179b24be2ec2eb56ff192ded83dfc9a61a20680c083

Observation 812e2cb7-61b4-4108-9960-f10e9ff0f325 · outbound

This paper cites Justeson and Slava M.

Relation Geometry in Semantic Space of Language Models Justeson and Slava M

Reference 66

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no resolver link, observed 2026-07-30T21:52:45.936591Z

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source=arxiv_source observed=2026-07-30T21:52:45.936591Z digest=sha256:65e7d31d3a18fa9b40ad4c78683f745feffa441476e1061e6f82a9d7e09b055c

Observation 678e6f30-a9f0-4079-83fd-0f2d98d63011 · outbound

This paper cites Miller and Walter G.

Relation Geometry in Semantic Space of Language Models Miller and Walter G

Reference 67

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doi, observed 2026-07-30T21:56:18.354015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.939734Z digest=sha256:2ef1a483d8442db8d99119976dd83d581f4b38a4854a2112e32a3d4ec87c5278

Observation 15221cfb-71e3-40a2-b4c0-2518e9e45d05 · outbound

This paper cites Antonymy:.

Relation Geometry in Semantic Space of Language Models Antonymy:

Reference 68

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no resolver link, observed 2026-07-30T21:52:45.943630Z

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source=arxiv_source observed=2026-07-30T21:52:45.943630Z digest=sha256:2d7d619eb97d8e9a3dac6e147d79399bf394f731dff9e0841b83a9c18938c885

Observation 444d60df-d2ec-40f5-80d1-5d5dea65ed8e · outbound

This paper cites Biometrical Journal , volume =.

Relation Geometry in Semantic Space of Language Models Biometrical Journal , volume =

Reference 69

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source=arxiv_source observed=2026-07-30T21:52:45.946763Z digest=sha256:269d4b43b2343d9f60c9095b602c773e210750acee71ae9480efbf588266e91a

Observation ef9214f8-379a-4273-ac92-d136b571c8f0 · outbound

This paper cites How Do Large Language Models Acquire Factual Knowledge During Pretraining? , url =.

Relation Geometry in Semantic Space of Language Models How Do Large Language Models Acquire Factual Knowledge During Pretraining? , url =

Reference 70

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source=arxiv_source observed=2026-07-30T21:52:45.950181Z digest=sha256:a7eeacc77859b86730da0540375a4936c96d5db8e5b47ae7d989bb706767ec9b

Observation 2ed8e847-9768-49a6-bc22-0c654d42c6b7 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

Relation Geometry in Semantic Space of Language Models The Thirteenth International Conference on Learning Representations , year=

Reference 71

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source=arxiv_source observed=2026-07-30T21:52:45.953697Z digest=sha256:d16ce20bc5e2f6baa3b0f1188d55c44b3459c14638314c6efbee541c3ae38ecd

Observation 7c9da1c4-57f0-439d-a97f-6b813ee30e42 · outbound

This paper cites Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations.

Relation Geometry in Semantic Space of Language Models Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations

Reference 72

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verified exact
doi, observed 2026-07-30T21:56:18.333848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 3d4d9cf9-8094-4836-928f-5fca9f2994ad · outbound

This paper cites Antonym sequence in written discourse: a corpus-based study.

Relation Geometry in Semantic Space of Language Models Antonym sequence in written discourse: a corpus-based study

Reference 73

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 09f02d03-f1de-4e97-a2d3-045a962ddf78 · outbound

This paper cites On Log-Likelihood-Ratios and the Significance of Rare Events.

Relation Geometry in Semantic Space of Language Models On Log-Likelihood-Ratios and the Significance of Rare Events

Reference 74

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no resolver link, observed 2026-07-30T21:52:45.965016Z

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source=arxiv_source observed=2026-07-30T21:52:45.965016Z digest=sha256:c8af9fbf24feab758189f2fdfeb8483c5d61ec2145c1ba76d261e3c96ecdf844

Observation 05dfcc77-2daa-45d2-b9cc-2e22e5920413 · outbound

This paper cites Corpora and collocations , volume =.

Relation Geometry in Semantic Space of Language Models Corpora and collocations , volume =

Reference 75

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no resolver link, observed 2026-07-30T21:52:45.968269Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-30T21:52:45.968269Z digest=sha256:27f739fca42b2793abce4398fc6a211ff2d56cc448752c35fd4c24a5d9a64ce4

Observation 066dbeba-470a-4928-893c-b56afacb3c24 · outbound

This paper cites Accurate Methods for the Statistics of Surprise and Coincidence.

Relation Geometry in Semantic Space of Language Models Accurate Methods for the Statistics of Surprise and Coincidence

Reference 76

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.971512Z digest=sha256:390328667cc7985f028d8780f4d1b60111d62e13500099fe001cca65e481b6a7

Observation e5ba774f-8135-4f6c-bbc8-10fb1e9e0ea7 · outbound

This paper cites An in-depth look into the co-occurrence distribution of semantic associates , volume =.

Relation Geometry in Semantic Space of Language Models An in-depth look into the co-occurrence distribution of semantic associates , volume =

Reference 77

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no resolver link, observed 2026-07-30T21:52:45.975064Z

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source=arxiv_source observed=2026-07-30T21:52:45.975064Z digest=sha256:40eea2b1520eb9d86c8f991c50d557a454ca1625a8ea8cf749c0a2b40dcd803f

Observation 218870b0-3d39-40a5-ad08-d77a79aa8104 · outbound

This paper cites Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection.

Relation Geometry in Semantic Space of Language Models Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

Reference 78

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no resolver link, observed 2026-07-30T21:52:45.978392Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.978392Z digest=sha256:be45b1181164ddb26ff7d1e64a9e4a445102fe0be413923011e73d0b347c7638

Observation 8378e6fe-ba7d-4a2c-a692-c2117f862711 · outbound

This paper cites an unresolved cited work.

Relation Geometry in Semantic Space of Language Models Unresolved cited work

Reference 79

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verified exact
doi, observed 2026-07-30T21:56:18.301169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation c4cb126f-ea43-4c15-af9c-708d4ec09aaa · outbound

This paper cites Linear Algebraic Structure of Word Senses, with Applications to Polysemy.

Relation Geometry in Semantic Space of Language Models Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Reference 80

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:45.985758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.985758Z digest=sha256:29152f7394d712d18892835af01de8862cc6dff346b3b909993f22e6432d0a5b

Observation ed5c06c9-2ffb-4bd9-b695-fb8b3d7fd347 · outbound

This paper cites A New Formulation of Z ipf ' s Meaning-Frequency Law through Contextual Diversity.

Relation Geometry in Semantic Space of Language Models A New Formulation of Z ipf ' s Meaning-Frequency Law through Contextual Diversity

Reference 81

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:45.988994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.988994Z digest=sha256:3585903232f0688d07e27993f3840df4dc783474b097659aa9e4affe1ffda3ac

Observation 6fa5940c-d2dd-4441-b516-5d706e08155a · outbound

This paper cites Analysis and Evaluation of Language Models for Word Sense Disambiguation.

Relation Geometry in Semantic Space of Language Models Analysis and Evaluation of Language Models for Word Sense Disambiguation

Reference 82

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.271340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.992486Z digest=sha256:c88b44ae01c3e1cf107af98043dd0730912a1092cdd47543cde8421e6b650d81

Observation 9dd708ab-c684-4164-9d04-af620b92a1ac · outbound

This paper cites Towards Understanding Linear Word Analogies.

Relation Geometry in Semantic Space of Language Models Towards Understanding Linear Word Analogies

Reference 83

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.258908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:45.995999Z digest=sha256:b3ce7888d648a2a9b5930286f9358dea68f0dabb320ac97f0d549a2cb39a453b

Observation 60928bec-d55c-4a2c-932b-77ced4f40201 · outbound

This paper cites Identifying Linear Relational Concepts in Large Language Models.

Relation Geometry in Semantic Space of Language Models Identifying Linear Relational Concepts in Large Language Models

Reference 84

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:45.999294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:45.999294Z digest=sha256:12c3e2e1f9062d68333bd891e50eaf02f2f97fc8284bb41a345d68e26177f427

Observation 189e6d3a-47bd-4c18-9cc7-467e8b2069a6 · outbound

This paper cites Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning.

Relation Geometry in Semantic Space of Language Models Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning

Reference 85

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unresolved
no resolver link, observed 2026-07-30T21:52:46.002576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.002576Z digest=sha256:a538de6b29a033f949261dd518331286574cffeb916626d06faf317b5df1d7a6

Observation ac6337cb-7dcc-405b-9f2c-4f93e0cecb8f · outbound

This paper cites Proceedings of the 40th International Conference on Machine Learning , articleno =.

Relation Geometry in Semantic Space of Language Models Proceedings of the 40th International Conference on Machine Learning , articleno =

Reference 86

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:46.005739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.005739Z digest=sha256:cdb4ce0053fb211dd4ebc3e0a7440df482184480ac15aed200f97669bd7dd873

Observation 28fb2daa-e9e0-423b-843d-6cc79949794a · outbound

This paper cites 1975 , issn =.

Relation Geometry in Semantic Space of Language Models 1975 , issn =

Reference 87

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.238284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.008788Z digest=sha256:c99014f26b59a704092cd2f5ee662268dcd45f2520c1800a0fe0b211b8a363f8

Observation 802eae69-e209-47c1-b124-3bdcd79c2ae9 · outbound

This paper cites Cognitive representations of semantic categories.

Relation Geometry in Semantic Space of Language Models Cognitive representations of semantic categories

Reference 88

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unresolved
no resolver link, observed 2026-07-30T21:52:46.012923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.012923Z digest=sha256:d5940964aec10527a35c41ca7a95afda71d3d1464500a7ab57fc7c43dd65e40f

Observation de44feec-d56e-49f7-b937-6aca70d63052 · outbound

This paper cites an unresolved cited work.

Relation Geometry in Semantic Space of Language Models Unresolved cited work

Reference 89

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.217630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.016100Z digest=sha256:8a76fe7083f2360686f9d97e21db007a715a335f7cbce7874e8b71a978bc3a0e

Observation f751c8fd-6770-4ad0-b9dc-366a3cec62c0 · outbound

This paper cites Antonymy and Canonicity: Experimental and Distributional Evidence.

Relation Geometry in Semantic Space of Language Models Antonymy and Canonicity: Experimental and Distributional Evidence

Reference 90

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:46.019338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.019338Z digest=sha256:a82fe3aa3ebc0df6cb831ad6c2932083b0cb72922eef66428e9ea466ec4798f7

Observation d069c6b5-c384-44c3-8cd0-8040aa1696e8 · outbound

This paper cites Good and Bad Opposites: Using Textual and Experimental Techniques to Measure Antonym Canonicity.

Relation Geometry in Semantic Space of Language Models Good and Bad Opposites: Using Textual and Experimental Techniques to Measure Antonym Canonicity

Reference 91

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.205429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.022497Z digest=sha256:56ab2de60999e824c1eb9feaacb25f98faac6ebda830c57c584fcc46875cb6a8

Observation 6710f108-b466-4727-baf7-4f25444b3075 · outbound

This paper cites Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models , url =.

Relation Geometry in Semantic Space of Language Models Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models , url =

Reference 92

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.193871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.025762Z digest=sha256:832d9d370a7a7442fd9bf69f80e9acc1a421904a1e06703c6643f2d8bdea885b

Observation af7f8a62-78ce-4a37-bf3f-500e44aadfdb · outbound

This paper cites and Leacock, Claudia and Tengi, Randee and Bunker, Ross T.

Relation Geometry in Semantic Space of Language Models and Leacock, Claudia and Tengi, Randee and Bunker, Ross T

Reference 93

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unresolved
no resolver link, observed 2026-07-30T21:52:46.029177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.029177Z digest=sha256:9a3e8984178a467232a503068520cfa54e52a27adbb3667c743a28fb359d6e41

Observation b645c482-7383-4806-bc18-95e30701afbd · outbound

This paper cites Psychological Methods , author =.

Relation Geometry in Semantic Space of Language Models Psychological Methods , author =

Reference 94

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.181738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.032224Z digest=sha256:58f0447701ba7e310d7dcfde34e3d2741998638ea10dfa336d23f971d19e6b85

Observation 741011d2-c8e8-4756-9762-6a29bb16e9d0 · outbound

This paper cites Do Supervised Distributional Methods Really Learn Lexical Inference Relations?.

Relation Geometry in Semantic Space of Language Models Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

Reference 95

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.169886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.035747Z digest=sha256:c406a61633baed2b6aa7e3e06b6665485d5c93974688b543c6e58ef0795d4d75

Observation dcb8a145-f42d-4e5f-865d-10d8c3a169d5 · outbound

This paper cites Transparency Helps Reveal When Language Models Learn Meaning.

Relation Geometry in Semantic Space of Language Models Transparency Helps Reveal When Language Models Learn Meaning

Reference 96

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.157695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.039389Z digest=sha256:f1011aa708d52bcd70e6433473c700a6a9bf23cc8804858e98a4663bfc6e989f

Observation 505c6964-f677-40ab-9ffb-ff61351c8e22 · outbound

This paper cites What Does BERT Learn about the Structure of Language?.

Relation Geometry in Semantic Space of Language Models What Does BERT Learn about the Structure of Language?

Reference 97

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:46.042628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.042628Z digest=sha256:c9d375a940f4d36f3be498f6794177d51ef058761cd2c7b7098bd84024cc860d

Observation 9ac5bcaa-a0a8-4310-bd5a-4236de63352c · outbound

This paper cites Linguistic Blind Spots of Large Language Models.

Relation Geometry in Semantic Space of Language Models Linguistic Blind Spots of Large Language Models

Reference 98

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verified exact
doi, observed 2026-07-30T21:56:18.136086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.046042Z digest=sha256:40caa3defd3702071a10e8ed2815404592fe31f231f4c49aad5b783ac784bc42

Observation c82fff4d-5e54-4d75-b241-de68cc81362d · outbound

This paper cites 2024 , issue_date =.

Relation Geometry in Semantic Space of Language Models 2024 , issue_date =

Reference 99

Resolution
verified exact
doi, observed 2026-07-30T21:56:18.055025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-30T21:52:46.049366Z digest=sha256:7af8d7d6ac37992894222c89dd17c412bfbbc7ac2edefe016f4e208c616ef0aa

Observation 56ee5300-210e-4d5f-8def-2adb99754020 · outbound

This paper cites Translating Embeddings for Modeling Multi-relational Data , url =.

Relation Geometry in Semantic Space of Language Models Translating Embeddings for Modeling Multi-relational Data , url =

Reference 100

Resolution
unresolved
no resolver link, observed 2026-07-30T21:52:46.053547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.053547Z digest=sha256:c837b375def0b39e6d03835cb0bb569e7a6d118d10d3b990132609f7f5fb66d7

Observation 3392d44d-4194-4977-8434-926964c455a6 · outbound

This paper cites Proceedings of the International Conference on Learning Representations , year =.

Relation Geometry in Semantic Space of Language Models Proceedings of the International Conference on Learning Representations , year =

Reference 101

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unresolved
no resolver link, observed 2026-07-30T21:52:46.056544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:52:46.056544Z digest=sha256:c69918feeca1dd93075f1e2d357c7254d68afe800d07301674f633fcd4eff9b7

Pith citing papers

No inbound Pith citation observations are available.