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Paper Citation Record · LEDGER

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2411.12395.

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

pith.paper-citation-record.v1
2411.12395 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:39:08.452490Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T11:06:38.102348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T11:11:27.550895Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dd79d9b1-7380-466f-ab58-c8a12865509f · outbound

This paper cites 56% of college students have used AI on assignments or exams: Bestcolleges,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering 56% of college students have used AI on assignments or exams: Bestcolleges,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.907569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.358990Z digest=sha256:5774f4cf37e7dc9d9963f2256af08808996c501525ce624dd56e9e91e916a7ad

Observation b7fba134-b158-4159-9e82-7fdd89f28d8f · outbound

This paper cites Manjrekar.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Manjrekar

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.894498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.363513Z digest=sha256:c2bb058c559d3a725d337127942e87004465bcf7c67fd057fed3c007830a52cb

Observation 120c83de-044f-4cd6-9322-4df3b20c7f5a · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.367529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.367529Z digest=sha256:f0fdbd594308b2d245990da5fbdb7e36dab803ea5682a2347b68a79dcd99923d

Observation a434acd6-0841-4cf4-a452-fb353b979dcf · outbound

This paper cites Attention is all you need,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Attention is all you need,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.371709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.371709Z digest=sha256:25c30b31dad17262842244efea6bfb8ec7d1029ab96ffa85416cc8f10d31fe41

Observation a06bbfa9-ee43-40a4-8667-340e2f005c4b · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.376028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.376028Z digest=sha256:0ce125dcec1dcf1e32b2e144a872501fb389b8701efbcef882cca6fb90bc1720

Observation 4c81cbad-95bf-491a-9a14-bb9bdbebf415 · outbound

This paper cites Language models are unsupervised multitask learners,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Language models are unsupervised multitask learners,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.381029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.381029Z digest=sha256:4d208b70ee652655353ef0cf906ba8c4fe7135f15a5bd0380fdf695506e8e17b

Observation edf07336-dc48-4ab6-9669-655e786389f1 · outbound

This paper cites Language Models are Few-Shot Learners.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Language Models are Few-Shot Learners

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.385458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.385458Z digest=sha256:041a4466e71340ff9fe704997095dea42447eafa32e8c4b6c73bf047c70c7126

Observation 3e003c7c-18cb-4c5c-bd27-11ade923dab2 · outbound

This paper cites GPT-4 Technical Report.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering GPT-4 Technical Report

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.389307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.389307Z digest=sha256:c312b4d658219f02dcf86d57aebbbf365b2fb7f226ad448c078bcb164785cec3

Observation 03695bbc-4bfc-4842-bc74-c5760b1055bb · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering LLaMA: Open and Efficient Foundation Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.393263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.393263Z digest=sha256:bae03e7128ba30de0a4dec06e2802801bc2cf508d601cc662d9f3611a90aeff0

Observation 3e7a26aa-8f41-4065-9a68-49dedbd208c0 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.397186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.397186Z digest=sha256:97db280fa0d07e42dc14dddbbbbcfb4b744d92077c41719736d05b96b8f2b4ad

Observation 42446af3-b266-4cf3-8a5b-9d7181cdb463 · outbound

This paper cites Instruction tuning for large language models: A survey,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Instruction tuning for large language models: A survey,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.401394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.401394Z digest=sha256:e986ad94b3dd3ab204792782f4690650169787c887898b82f23806ede6bf42f8

Observation 115bdd92-df93-46d8-a240-b2764aad0616 · outbound

This paper cites Fighting fire with fire: can chatgpt detect ai-generated text?.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Fighting fire with fire: can chatgpt detect ai-generated text?

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.863853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.405159Z digest=sha256:ac39f750a9e82a26d182236b78d369b655a825d1e5cab3df634cade0d36e96f1

Observation b790d220-7b46-4551-bbe7-eadfd1481f07 · outbound

This paper cites MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.408800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.408800Z digest=sha256:2be6c358fa76cf25a3b669e3e531a4845444388793ea37af5c3899d598d40ca9

Observation 3285a012-fc59-4c10-93a6-87c5269e170f · outbound

This paper cites The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.412464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.412464Z digest=sha256:8b18b0ee2cb05d3a1cfb2d16ab82856457e4ac0765c1253d3e5592e5c8cec1f8

Observation 0c58a8ba-7cf8-4d05-b7e5-c781e7ea4439 · outbound

This paper cites Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.850880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.416156Z digest=sha256:add0d6dcee7913d05ec20ec4bf354efe4defcb8fb9fd0b58da440e997fa0f419

Observation fcde1c31-717f-4f56-923b-320ea5e2a6f3 · outbound

This paper cites Task Ambiguity in Humans and Language Models.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Task Ambiguity in Humans and Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.419760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.419760Z digest=sha256:4c30c7430f52d4b67f11e5aab4d6ec8a1bfb606b0fa80208b4ccf47e7089f566

Observation 2d443af0-68d7-495b-acff-79f44d8a38d4 · outbound

This paper cites an unresolved cited work.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-12T17:39:08.838025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.423686Z digest=sha256:1bfe7d86886de66e3a3cdcabad42919dee4b236bf9456cc8bd22a9d6734b3459

Observation 60877db6-650f-475a-8a3c-eae3a2054af7 · outbound

This paper cites The winograd schema challenge,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering The winograd schema challenge,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.824823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.427497Z digest=sha256:effcecee3c0f2977f14ef9570e01cd75a57d812d6cd2b8dd5d1eabeaa80d9595

Observation 5d86b036-0ab5-48da-9192-963695f02871 · outbound

This paper cites Gpt-4o system card,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Gpt-4o system card,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.810453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.431031Z digest=sha256:aec7e4b33096e33495e72290c7b4f00f00ed14a7d2e67243ff06772f9852c1b3

Observation f4e1e5be-6b1d-4cca-8047-6bad97f4e02c · outbound

This paper cites Natural questions: a benchmark for question answering research,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Natural questions: a benchmark for question answering research,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.797087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.434415Z digest=sha256:eee6571e3cfd20ea2ac08f967cc11e251c3ea3e03211bcd05bf93512ef31216c

Observation 350d6fbc-e570-4e7b-b330-34ca0f244b95 · outbound

This paper cites AmbigQA: Answering ambiguous open-domain questions,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering AmbigQA: Answering ambiguous open-domain questions,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.783784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.438058Z digest=sha256:59f628bb667b73f2572a32b8485259ef6f06e440588b7b0b31144927d268271e

Observation 7a9ec339-6cfe-4d92-9cba-42f986aae223 · outbound

This paper cites Scope ambiguities in large language models,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Scope ambiguities in large language models,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.770656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.441544Z digest=sha256:99a2cb8e43160c12b88542af0260448699c2ea3b027988be660e3877fd061849

Observation 39706813-4643-43d5-bcda-7d4ecd053b7f · outbound

This paper cites Mistral 7B.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Mistral 7B

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.445089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.445089Z digest=sha256:19626593d79357996f228ca391681e9d9cd67631e03e863d8c1d296d8ea8bc72

Observation e4113f78-fd4e-47d4-a15b-9c74d6b13237 · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-12T17:39:08.448804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:39:08.448804Z digest=sha256:71ce88c0e1630827145080333c10902ef15c3341eb34690996b22cd3eec0506c

Observation 142c4aa5-8cac-46e6-bf93-9ae4a50f416c · outbound

This paper cites Preserving principal subspaces to reduce catastrophic forgetting in fine-tuning,.

Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Preserving principal subspaces to reduce catastrophic forgetting in fine-tuning,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:39:08.757445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-12T17:39:08.452490Z digest=sha256:5f244599a4f4ab1abd18ccbfd1735f585ed26d076ca2a47420bc185f4867d820

Pith citing papers

Observation 65801f10-9aa9-4555-ae5a-e38a67416de9 · inbound

VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction cites this paper.

VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-22T11:11:27.554087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-22T11:06:38.102348Z digest=sha256:90d4de2bff4ebee9370f48312af41a1e71a607f75f4d766568b16699b91a2985