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Paper Citation Record · LEDGER

BELL: Benchmarking the Explainability of Large Language Models

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2504.18572.

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

pith.paper-citation-record.v1
2504.18572 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:21:09.834127Z

measured 37 of 37 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 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

37 of 37 outbound references displayed

  • verified exact0
  • verified fuzzy16
  • unresolved21
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 98efdbdd-028e-4080-90a2-8aa8efd840de · outbound

This paper cites Natural language processing: State of the art, current trends and challenges.

BELL: Benchmarking the Explainability of Large Language Models Natural language processing: State of the art, current trends and challenges

Reference 1

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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.

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Observation e0e9772e-993e-4570-8202-8fb6fb26c4fe · outbound

This paper cites Multilingual machine translation with large language models: Empirical results and analysis, 2023.

BELL: Benchmarking the Explainability of Large Language Models Multilingual machine translation with large language models: Empirical results and analysis, 2023

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:21:09.680861Z digest=sha256:443abd6e931c1d0eb02340f4518eec7993cd5fd5c96d7934df69a2f2b4473f46

Observation 90a70ab8-e798-4eca-8c9c-189eaee12bc1 · outbound

This paper cites Wordcraft: story writing with large language models.

BELL: Benchmarking the Explainability of Large Language Models Wordcraft: story writing with large language models

Reference 3

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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=2026-08-16T11:21:09.685409Z digest=sha256:e446bbd1aad984aa073b9c5220daea4e9a46f4b1b4fbb47479a8df18eaeb14fb

Observation a5a355d9-353a-46cb-8f84-ef2e8518e0b0 · outbound

This paper cites https://medium.com/whatnot - engineering/enhancing-search-using-large-language-models-f9dcb988bdb9.

BELL: Benchmarking the Explainability of Large Language Models https://medium.com/whatnot - engineering/enhancing-search-using-large-language-models-f9dcb988bdb9

Reference 4

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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=2026-08-16T11:21:09.690348Z digest=sha256:4752e4d713cb6dbd9287f7edd06e46227a24fa7fff066f70a98c3af4cc13112a

Observation 98c32250-c170-4b33-96a1-35ef5b150813 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

BELL: Benchmarking the Explainability of Large Language Models Code Llama: Open Foundation Models for Code

Reference 5

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source=pdf_text observed=2026-08-16T11:21:09.694984Z digest=sha256:2763bb7f168da815e5dc536679b4d82c8883a2277502837f37f0428cb0e7372b

Observation 36ee1f7b-e6a1-41ab-bba3-43f89a885d4d · outbound

This paper cites Bloomberggpt: A large language model for finance, 2023.

BELL: Benchmarking the Explainability of Large Language Models Bloomberggpt: A large language model for finance, 2023

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0703a95a-74aa-4f88-b36c-c5598e2258bf · outbound

This paper cites The impact of large language models on scientific discovery: a preliminary study using gpt-4, 2023.

BELL: Benchmarking the Explainability of Large Language Models The impact of large language models on scientific discovery: a preliminary study using gpt-4, 2023

Reference 7

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Observation 226de855-8c00-4e3b-bf8b-1c17a7b76954 · outbound

This paper cites Pllama: An open -source large language model for plant science, 2024.

BELL: Benchmarking the Explainability of Large Language Models Pllama: An open -source large language model for plant science, 2024

Reference 8

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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=2026-08-16T11:21:09.709185Z digest=sha256:cc39f00f2d0f611dbe27b47cb95e124b6779b5a2cd200e47692f06c8985af3a4

Observation fc13c7a3-9bbd-49ca-972c-4255cda2c17d · outbound

This paper cites Artgpt-4: Artistic vision-language understanding with adapter-enhanced minigpt-4, 2023.

BELL: Benchmarking the Explainability of Large Language Models Artgpt-4: Artistic vision-language understanding with adapter-enhanced minigpt-4, 2023

Reference 9

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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.

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Observation 157f1900-e870-4cf3-bce3-25772c0224bf · outbound

This paper cites Taoli llama.

BELL: Benchmarking the Explainability of Large Language Models Taoli llama

Reference 10

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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.

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Observation 769cd097-00a7-4cb4-9f01-9acaa6065ac0 · outbound

This paper cites Marinegpt: Unlocking secrets of “ocean” to the public, 2023.

BELL: Benchmarking the Explainability of Large Language Models Marinegpt: Unlocking secrets of “ocean” to the public, 2023

Reference 11

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

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Observation 51c88f23-8d03-44d4-88b4-fa72ec605517 · outbound

This paper cites Disc-lawllm: Fine-tuning large language models for intelligent legal services, 2023.

BELL: Benchmarking the Explainability of Large Language Models Disc-lawllm: Fine-tuning large language models for intelligent legal services, 2023

Reference 12

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

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Observation ebb52783-b724-402d-82d1-b57fc0eb3ad0 · outbound

This paper cites Large language models and political science.

BELL: Benchmarking the Explainability of Large Language Models Large language models and political science

Reference 13

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Source-reported events for the cited work

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Observation 4a57423f-20f1-46af-a1e8-9f67963cf49a · outbound

This paper cites Alpacare:instruction-tuned large language models for medical application, 2023.

BELL: Benchmarking the Explainability of Large Language Models Alpacare:instruction-tuned large language models for medical application, 2023

Reference 14

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source=pdf_text observed=2026-08-16T11:21:09.733025Z digest=sha256:f92cdff9516e5c22d70b6088161bf7435fe648e35f25f08b282dbe30d34daa77

Observation fc4c6b75-4d0d-4462-ac74-944233082852 · outbound

This paper cites Davison, Quanzheng Li, Yong Chen, Hongfang Liu, and Lichao Sun.

BELL: Benchmarking the Explainability of Large Language Models Davison, Quanzheng Li, Yong Chen, Hongfang Liu, and Lichao Sun

Reference 15

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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.

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Observation 4a889158-793c-47c8-b413-7485049af38a · outbound

This paper cites Factuality challenges in the era of large language models, 2023.

BELL: Benchmarking the Explainability of Large Language Models Factuality challenges in the era of large language models, 2023

Reference 16

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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.

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Observation 91025a1d-f9c2-4db6-9fc9-e5e0f3305169 · outbound

This paper cites Unraveling the link between translations and gender bias in llms, 2023.

BELL: Benchmarking the Explainability of Large Language Models Unraveling the link between translations and gender bias in llms, 2023

Reference 17

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Observation 409c95cb-a209-4132-87c8-0a51d80203b8 · outbound

This paper cites Jailbroken: How Does LLM Safety Training Fail?.

BELL: Benchmarking the Explainability of Large Language Models Jailbroken: How Does LLM Safety Training Fail?

Reference 18

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Observation e367df9f-18f4-490b-9bdd-909e86452bb0 · outbound

This paper cites an unresolved cited work.

BELL: Benchmarking the Explainability of Large Language Models Unresolved cited work

Reference 19

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

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Observation 1c91cfb2-46f9-4b80-aaa6-d13192a878c7 · outbound

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

BELL: Benchmarking the Explainability of Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 20

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Observation 6f865b43-d8de-4d45-b35a-3738878826ed · outbound

This paper cites Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations.

BELL: Benchmarking the Explainability of Large Language Models Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

Reference 21

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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.

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Observation 1580d568-7c04-4c3c-a295-00c439476217 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

BELL: Benchmarking the Explainability of Large Language Models Large Language Models are Zero-Shot Reasoners

Reference 22

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

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Observation 59ae4b4f-5cb9-49b5-9711-7ebd9097a4f4 · outbound

This paper cites Soft-prompt Tuning for Large Language Models to Evaluate Bias.

BELL: Benchmarking the Explainability of Large Language Models Soft-prompt Tuning for Large Language Models to Evaluate Bias

Reference 23

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source=pdf_text observed=2026-08-16T11:21:09.770240Z digest=sha256:1318d36ae1a9bdea4229c4704bec4dfb31c4fa519c44b060088f9949436ad7c5

Observation f70a547d-3c60-4929-9829-d92d18e01aad · outbound

This paper cites Chi, Quoc V.

BELL: Benchmarking the Explainability of Large Language Models Chi, Quoc V

Reference 24

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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.

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Observation d8f37bf0-8bda-488b-a542-de327ac31989 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

BELL: Benchmarking the Explainability of Large Language Models Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 25

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Observation 8f329d8a-3870-4189-8e0a-5fd9d567fb28 · outbound

This paper cites Graph of Thoughts: Solving Elaborate Problems with Large Language Models.

BELL: Benchmarking the Explainability of Large Language Models Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Reference 26

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Observation 05dd6804-b44b-4a78-a7bf-291dd05c07a5 · outbound

This paper cites Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models.

BELL: Benchmarking the Explainability of Large Language Models Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models

Reference 27

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source=pdf_text observed=2026-08-16T11:21:09.788504Z digest=sha256:0db75f83c2c330def8120e30404a2b5745e01cfcd78b9178a9b94f70e21f70cb

Observation 00ae8f80-cef1-4fe5-9ab7-00638f63aca0 · outbound

This paper cites Le, and Ed H.

BELL: Benchmarking the Explainability of Large Language Models Le, and Ed H

Reference 28

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raw_fallback, observed 2026-08-16T11:21:10.101525Z

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

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Observation 027cb75c-6c49-4b96-8792-f2d4886c5164 · outbound

This paper cites Self -consistency improves chain of thought reasoning in Infosys Responsible AI Office language models.

BELL: Benchmarking the Explainability of Large Language Models Self -consistency improves chain of thought reasoning in Infosys Responsible AI Office language models

Reference 29

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raw_fallback, observed 2026-08-16T11:21:10.086006Z

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.

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Observation 29e7abaa-4027-4808-bdcc-512f4c2c9064 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

BELL: Benchmarking the Explainability of Large Language Models Star: Bootstrapping reasoning with reasoning

Reference 30

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Observation ae236c1a-ef04-456c-8d9c-a5246a769a16 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

BELL: Benchmarking the Explainability of Large Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 31

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Observation 634891e2-62f2-4fc5-b9c3-0a1ab0457a66 · outbound

This paper cites Thread of Thought Unraveling Chaotic Contexts.

BELL: Benchmarking the Explainability of Large Language Models Thread of Thought Unraveling Chaotic Contexts

Reference 32

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source=pdf_text observed=2026-08-16T11:21:09.811047Z digest=sha256:e34338581e65d1b93bd464bccb65184fb0abe95bea3751164440fd43d02f0374

Observation 9a816261-d817-489b-b40f-4152c574f2fd · outbound

This paper cites an unresolved cited work.

BELL: Benchmarking the Explainability of Large Language Models Unresolved cited work

Reference 33

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no resolver link, observed 2026-08-16T11:21:09.815808Z

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source=pdf_text observed=2026-08-16T11:21:09.815808Z digest=sha256:a0fae3c6d74e8ebc9e7748e26cb9a935fb13609524ef7bac2e27922dae58ebeb

Observation e8dd44d4-d7fa-4777-a87a-b59641704a50 · outbound

This paper cites Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models.

BELL: Benchmarking the Explainability of Large Language Models Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models

Reference 34

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source=pdf_text observed=2026-08-16T11:21:09.820238Z digest=sha256:6550653a52d421d774bd6e9d4e27d5f0a81ad904a7bce0cc35c15a56bedc0d41

Observation 4a54889c-6f82-4d57-862f-9d0e39eb0df4 · outbound

This paper cites an unresolved cited work.

BELL: Benchmarking the Explainability of Large Language Models Unresolved cited work

Reference 35

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raw_fallback, observed 2026-08-16T11:21:10.061014Z

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=2026-08-16T11:21:09.825142Z digest=sha256:da1e9ff6ed7c15f5f9bd28785344a31ae10742440c9c35a60ddfc0c4b40b471d

Observation ad1ca8c4-2239-4269-84eb-d24dde5154a3 · outbound

This paper cites an unresolved cited work.

BELL: Benchmarking the Explainability of Large Language Models Unresolved cited work

Reference 36

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no resolver link, observed 2026-08-16T11:21:09.829655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

BELL: Benchmarking the Explainability of Large Language Models Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 37

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