Paper Citation Record · LEDGER

GPT Understands, Too

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2103.10385.

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

pith.paper-citation-record.v1
2103.10385 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 34 of 34 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 34 of 34 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T10:43:49.230377Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:00:01.488346Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 278d1412-9c50-403a-b232-b2412d795d7d · inbound

The Power of Scale for Parameter-Efficient Prompt Tuning cites this paper.

The Power of Scale for Parameter-Efficient Prompt Tuning GPT Understands, Too

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:34:05.315601Z

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.

Observation 8543792e-2e95-4428-b62b-32b9bc96da80 · inbound

Cross-Task Generalization via Natural Language Crowdsourcing Instructions cites this paper.

Cross-Task Generalization via Natural Language Crowdsourcing Instructions GPT Understands, Too

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:57:29.423919Z

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.

Observation 994a3588-df0a-47c3-bad9-0bbdf31393a5 · inbound

LoRA: Low-Rank Adaptation of Large Language Models cites this paper.

LoRA: Low-Rank Adaptation of Large Language Models GPT Understands, Too

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:01:40.853496Z

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.

Observation 35a825f4-5f8b-4bf9-bcae-a1341999a07d · inbound

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? cites this paper.

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? GPT Understands, Too

Reference 119

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:51:46.812580Z

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.

Observation 14715225-a925-46c1-82f9-336b1970bf3d · inbound

OPT: Open Pre-trained Transformer Language Models cites this paper.

OPT: Open Pre-trained Transformer Language Models GPT Understands, Too

Reference 133

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:53:17.511748Z

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.

Observation 5b546b10-9fa6-4563-b150-591bb119f591 · inbound

On the Power of Foundation Models cites this paper.

On the Power of Foundation Models GPT Understands, Too

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-24T10:49:21.367677Z

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.

Observation acc6c81f-25a2-4da5-9070-8e02225d61ca · inbound

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention cites this paper.

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention GPT Understands, Too

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:07:42.957216Z

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.

Observation 02374def-aefd-4bb4-81d3-7a66ea08e631 · inbound

CodeT5+: Open Code Large Language Models for Code Understanding and Generation cites this paper.

CodeT5+: Open Code Large Language Models for Code Understanding and Generation GPT Understands, Too

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:26:57.553242Z

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.

Observation 68e2ee75-b0cb-4699-ab88-3c9e81e585b5 · inbound

Towards Expert-Level Medical Question Answering with Large Language Models cites this paper.

Towards Expert-Level Medical Question Answering with Large Language Models GPT Understands, Too

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:32:33.574956Z

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.

Observation 332b6033-3bb5-44ad-a446-1926af15c99a · inbound

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations cites this paper.

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations GPT Understands, Too

Reference 165

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:25:08.125832Z

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.

Observation 614fa362-09f2-4eb7-8048-f9da50b1d6dc · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models GPT Understands, Too

Reference 247

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:28:39.111826Z

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.

Observation ea55b48a-bd5f-4686-83f8-c8c6059f6665 · inbound

Large Language Models as Optimizers cites this paper.

Large Language Models as Optimizers GPT Understands, Too

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T00:04:31.289275Z

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.

Observation 4e89dd82-88f9-467a-aba7-05298eb67f0f · inbound

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers cites this paper.

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers GPT Understands, Too

Reference 110

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:11:49.596632Z

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.

Observation be038804-055f-4b3e-85ae-9776aeb4c6fa · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey GPT Understands, Too

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T11:32:37.170459Z

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.

Observation 0132b675-9373-4a42-b79f-ee839b5fd6de · inbound

Algorithm for Automatic Legislative Text Consolidation cites this paper.

Algorithm for Automatic Legislative Text Consolidation GPT Understands, Too

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T10:43:49.230377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd46e811-38e7-4e3c-94b8-0bd5234a3b44 · inbound

GCoT: Chain-of-Thought Prompt Learning for Graphs cites this paper.

GCoT: Chain-of-Thought Prompt Learning for Graphs GPT Understands, Too

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T10:55:16.529693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 394283c3-de36-4340-982d-b8d5fecfc65a · inbound

Towards an AI co-scientist cites this paper.

Towards an AI co-scientist GPT Understands, Too

Reference 158

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:02:45.036698Z

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.

Observation df80b815-bd39-4edd-8217-e50882f428ed · inbound

CoLA: Collaborative Low-Rank Adaptation cites this paper.

CoLA: Collaborative Low-Rank Adaptation GPT Understands, Too

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:54.160731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7195ee69-06f0-40ae-b106-87e014d5e461 · inbound

Speech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction cites this paper.

Speech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction GPT Understands, Too

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:37.005934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b097ff4e-d7d2-4902-b52d-fa29e6c142cc · inbound

MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition cites this paper.

MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition GPT Understands, Too

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:37:47.221424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59bd6dd2-3512-4fb5-9170-7e33a3bd086f · inbound

Leveraging Self-Attention for Input-Dependent Soft Prompting in LLMs cites this paper.

Leveraging Self-Attention for Input-Dependent Soft Prompting in LLMs GPT Understands, Too

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:17:23.562950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dc55529-a7b4-48d0-8678-a3fe1e5a9a97 · inbound

Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective GPT Understands, Too

Reference 140

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:44.258722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15791386-02e7-4150-a7a5-9aff854c70b1 · inbound

Impact of Fine-Tuning Methods on Memorization in Large Language Models cites this paper.

Impact of Fine-Tuning Methods on Memorization in Large Language Models GPT Understands, Too

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:31.122601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc9ae2a2-42ec-4b22-92ff-fd4837ab230f · inbound

Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages cites this paper.

Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages GPT Understands, Too

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T20:57:40.940440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5db0fa5e-0b63-4470-8681-2683fa1e87b7 · inbound

MemOS: A Memory OS for AI System cites this paper.

MemOS: A Memory OS for AI System GPT Understands, Too

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T08:20:22.920687Z

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.

Observation 5fa119f3-bd7d-47df-b8bd-38490191b742 · inbound

Time Series Foundation Models for Multivariate Financial Time Series Forecasting cites this paper.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting GPT Understands, Too

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T18:49:28.433587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96365679-88f0-4b75-b214-5796262c7356 · inbound

Modeling Code: Is Text All You Need? cites this paper.

Modeling Code: Is Text All You Need? GPT Understands, Too

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:11:49.012680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e02c1bdd-15f9-41cd-b6c8-5867938717e7 · inbound

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity cites this paper.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity GPT Understands, Too

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:01:28.027974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdba9e19-1253-4ff5-b5f6-95e1e8b71720 · inbound

TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation cites this paper.

TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation GPT Understands, Too

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T15:27:29.299358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7b612f1-0977-4874-b6e2-3d079af1127f · inbound

Vision Transformer Finetuning Benefits from Non-Smooth Components cites this paper.

Vision Transformer Finetuning Benefits from Non-Smooth Components GPT Understands, Too

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T03:48:09.556542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c958bf84-36f6-4c3d-89c1-3eca06e5411b · inbound

Graph Topology Information Enhanced Heterogeneous Graph Representation Learning cites this paper.

Graph Topology Information Enhanced Heterogeneous Graph Representation Learning GPT Understands, Too

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:55:51.368900Z

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.

Observation 9d33cd1b-6416-43dc-b2c4-0d6841d6a381 · inbound

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning cites this paper.

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning GPT Understands, Too

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:01:10.815971Z

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.

Observation 845761d5-237d-4441-a7df-118cf7345b6b · inbound

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning cites this paper.

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning GPT Understands, Too

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T19:56:11.280281Z

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.

Observation b81959d3-9742-4fb4-8b8f-bc5fb2b280ee · inbound

Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models cites this paper.

Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models GPT Understands, Too

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T18:00:01.489800Z

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.