Paper Citation Record · LEDGER

Enterprise Large Language Model Evaluation Benchmark

As of 13 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 4 inbound Pith citation observations for arXiv:2506.20274.

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

pith.paper-citation-record.v1
2506.20274 v1

Coverage vector

measured 95 of 95 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:56:38.270220Z

measured 99 of 99 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:35:22.448174Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T04:37:48.221956Z

Reference resolution

95 of 95 outbound references displayed

verified exact4
verified fuzzy8
unresolved83
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d26afb3-7c65-45cf-af7f-f3d6a4f49515 · outbound

This paper cites AutoBencher: Towards Declarative Benchmark Construction.

Enterprise Large Language Model Evaluation Benchmark AutoBencher: Towards Declarative Benchmark Construction

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:30.682305Z

Source-reported events for the cited work

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Observation c6a15c97-fa48-4dbc-9ddf-8c498d482acb · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Enterprise Large Language Model Evaluation Benchmark Measuring Massive Multitask Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:30.775105Z

Source-reported events for the cited work

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Observation 3533b566-c2bd-409a-bdab-a4e8118de933 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Enterprise Large Language Model Evaluation Benchmark Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:30.868056Z

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Observation 14870780-827b-4017-abfd-0c0bcb9f89e8 · outbound

This paper cites On the Measure of Intelligence.

Enterprise Large Language Model Evaluation Benchmark On the Measure of Intelligence

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:30.935899Z

Source-reported events for the cited work

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Observation d4c7ccc0-ea19-4d32-8541-39d64b58b2c4 · outbound

This paper cites Holistic Evaluation of Language Models.

Enterprise Large Language Model Evaluation Benchmark Holistic Evaluation of Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.015321Z

Source-reported events for the cited work

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Observation 13bc31fc-f521-45a7-b742-77be8b929ce6 · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Enterprise Large Language Model Evaluation Benchmark Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.088687Z

Source-reported events for the cited work

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Observation 95a207cb-5792-4b9d-897f-3cfd1bc98123 · outbound

This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.

Enterprise Large Language Model Evaluation Benchmark What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.178017Z

Source-reported events for the cited work

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Observation 3fab65a6-a87f-47b2-9c49-e0a52a4737b1 · outbound

This paper cites MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI- SCE Framework,.

Enterprise Large Language Model Evaluation Benchmark MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI- SCE Framework,

Reference 8

Resolution: verified exact; doi, observed 2026-08-06T22:56:39.440489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 133620ab-795b-4615-978d-344bff241c31 · outbound

This paper cites MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering.

Enterprise Large Language Model Evaluation Benchmark MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.339236Z

Source-reported events for the cited work

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Observation 50a3937c-3f72-4d2b-a6e3-f365929ce04a · outbound

This paper cites WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation.

Enterprise Large Language Model Evaluation Benchmark WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.407399Z

Source-reported events for the cited work

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Observation 64bb560a-523f-47bf-b193-92442c6303a7 · outbound

This paper cites PubMedQA:ADatasetforBiomedicalResearch Question Answering,.

Enterprise Large Language Model Evaluation Benchmark PubMedQA:ADatasetforBiomedicalResearch Question Answering,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.496753Z

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Observation 29b04a36-3183-401a-9980-2e31a5a015ba · outbound

This paper cites CMB: A Comprehensive Medical Benchmark in Chinese.

Enterprise Large Language Model Evaluation Benchmark CMB: A Comprehensive Medical Benchmark in Chinese

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.572070Z

Source-reported events for the cited work

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Observation 808de6df-1352-4d5b-8f3c-b91a92e3faab · outbound

This paper cites FinBen: A Holistic Financial Benchmark for Large Language Models.

Enterprise Large Language Model Evaluation Benchmark FinBen: A Holistic Financial Benchmark for Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.645688Z

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Observation 6ac76429-9aac-4f48-a940-fcf3674db5d0 · outbound

This paper cites FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models.

Enterprise Large Language Model Evaluation Benchmark FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.714437Z

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Observation 9e6287b6-52fe-4e2e-ad5a-024229d2e6e8 · outbound

This paper cites WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain.

Enterprise Large Language Model Evaluation Benchmark WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.776768Z

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Observation 3d95e4e9-f96e-43c7-a6e9-2e31fb50e29c · outbound

This paper cites BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark.

Enterprise Large Language Model Evaluation Benchmark BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.843525Z

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Observation 9facde69-b28d-44f8-a3bc-1d3a07f30978 · outbound

This paper cites XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters.

Enterprise Large Language Model Evaluation Benchmark XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.910853Z

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Observation 015cc8c0-345c-45d6-8f7a-578d5c23c810 · outbound

This paper cites PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance.

Enterprise Large Language Model Evaluation Benchmark PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:31.976188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 347076fd-8cd8-417d-a8ac-74666d142968 · outbound

This paper cites LexGLUE: A Benchmark Dataset for Legal Language Understanding in English,.

Enterprise Large Language Model Evaluation Benchmark LexGLUE: A Benchmark Dataset for Legal Language Understanding in English,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.063517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6df36f35-623e-40c6-8368-97f94e36972a · outbound

This paper cites A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction.

Enterprise Large Language Model Evaluation Benchmark A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.171821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c23ae7a-5ba5-4c5b-8fa2-fd50e0ddb26d · outbound

This paper cites LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Enterprise Large Language Model Evaluation Benchmark LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.246086Z

Source-reported events for the cited work

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Observation 22779890-ffe0-482e-977c-a20e2120b210 · outbound

This paper cites LawBench: Benchmarking Legal Knowledge of Large Language Models.

Enterprise Large Language Model Evaluation Benchmark LawBench: Benchmarking Legal Knowledge of Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.291657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd3d96f8-d9ed-4523-ad50-6c26dd1cbdd1 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Enterprise Large Language Model Evaluation Benchmark CMMLU: Measuring massive multitask language understanding in Chinese

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.344640Z

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Unavailable: canonical work link unavailable.

Observation b86644a4-87fc-48f9-8936-06d9325cf7b7 · outbound

This paper cites Evaluating the Performance of Large Language Models on GAOKAO Benchmark.

Enterprise Large Language Model Evaluation Benchmark Evaluating the Performance of Large Language Models on GAOKAO Benchmark

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.401251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18399b1f-3b3e-4e5f-ba14-dd2a9e07f63e · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

Enterprise Large Language Model Evaluation Benchmark C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.480672Z

Source-reported events for the cited work

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Observation 52654b07-9443-4d22-9af5-fcda18c54227 · outbound

This paper cites Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?,.

Enterprise Large Language Model Evaluation Benchmark Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.551128Z

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Unavailable: canonical work link unavailable.

Observation b12a7ce0-23cb-4a3a-8113-06fea8de0083 · outbound

This paper cites M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models.

Enterprise Large Language Model Evaluation Benchmark M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.619412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcb23d56-9a47-4491-989f-80b31d02cb38 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

Enterprise Large Language Model Evaluation Benchmark AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.689119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28337c23-44fe-46f3-9c7b-8e6ed0cc1f67 · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

Enterprise Large Language Model Evaluation Benchmark Large Language Models for Data Annotation and Synthesis: A Survey

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.777385Z

Source-reported events for the cited work

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Observation c3dea55e-8b96-4cb9-97c7-39ffa85710e5 · outbound

This paper cites On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey.

Enterprise Large Language Model Evaluation Benchmark On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.834913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2779ed39-6f83-4b3a-9777-762e05bc2e53 · outbound

This paper cites Comprehensive Exploration of Synthetic Data Generation: A Survey.

Enterprise Large Language Model Evaluation Benchmark Comprehensive Exploration of Synthetic Data Generation: A Survey

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.901945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10f2024b-5644-4319-aa21-14e4442a5c3d · outbound

This paper cites Best Practices and Lessons Learned on Synthetic Data.

Enterprise Large Language Model Evaluation Benchmark Best Practices and Lessons Learned on Synthetic Data

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.969986Z

Source-reported events for the cited work

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Observation f27ffcb3-2c43-4073-842a-4fc88a577f5b · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Enterprise Large Language Model Evaluation Benchmark Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.055751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23d3d0b9-f0db-4648-8fef-8789b05f1e9b · outbound

This paper cites Self-Prompting Large Language Models for Zero-Shot Open-Domain QA.

Enterprise Large Language Model Evaluation Benchmark Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:56:39.032496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5a2cbb23-2130-4378-9268-c3eaa9f2f25c · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Enterprise Large Language Model Evaluation Benchmark DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.202456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3080cb20-447a-448d-bb40-8afde03381d6 · outbound

This paper cites an unresolved cited work.

Enterprise Large Language Model Evaluation Benchmark Unresolved cited work

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.269035Z

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Observation bb953800-ef19-4e2d-836e-2652df147c47 · outbound

This paper cites I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm.

Enterprise Large Language Model Evaluation Benchmark I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.340671Z

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Observation d579f04e-ef9b-4f6d-8c31-8dd701c90115 · outbound

This paper cites Self-Rewarding Language Models.

Enterprise Large Language Model Evaluation Benchmark Self-Rewarding Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.409397Z

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Unavailable: canonical work link unavailable.

Observation 139a865c-3c99-47d3-bdc5-87120610f228 · outbound

This paper cites West-of-N: Synthetic Preferences for Self-Improving Reward Models.

Enterprise Large Language Model Evaluation Benchmark West-of-N: Synthetic Preferences for Self-Improving Reward Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.472641Z

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Observation c95bc744-5fe4-4afd-8009-e645732914b9 · outbound

This paper cites Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge.

Enterprise Large Language Model Evaluation Benchmark Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.549019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11769962-4521-4a78-a9fb-7147203d8028 · outbound

This paper cites Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies.

Enterprise Large Language Model Evaluation Benchmark Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.612212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49aad555-c701-4ef5-80e8-962f6355f413 · outbound

This paper cites Leveraging Large Language Models for Multiple Choice Question Answering.

Enterprise Large Language Model Evaluation Benchmark Leveraging Large Language Models for Multiple Choice Question Answering

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.668975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18075e64-31c7-42fe-a470-82412368bdc9 · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Enterprise Large Language Model Evaluation Benchmark Teaching Models to Express Their Uncertainty in Words

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.793508Z

Source-reported events for the cited work

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Observation 751f1980-b1fa-4945-b2dd-925e99eee639 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,.

Enterprise Large Language Model Evaluation Benchmark Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.873613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87cfb609-01c4-4750-a836-71ea6394694f · outbound

This paper cites Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely.

Enterprise Large Language Model Evaluation Benchmark Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.954784Z

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Observation b5e3b33e-fcfb-4fc6-981f-52e92037ccf3 · outbound

This paper cites MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation.

Enterprise Large Language Model Evaluation Benchmark MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.045454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bb223b9-f6ff-4e39-bc45-c28adff200c8 · outbound

This paper cites Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity.

Enterprise Large Language Model Evaluation Benchmark Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.123277Z

Source-reported events for the cited work

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Observation b02df137-4093-4e14-a4bf-7259a7925728 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

Enterprise Large Language Model Evaluation Benchmark Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.211230Z

Source-reported events for the cited work

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Observation 3896673e-55e5-450b-a4ae-94d15b69afc7 · outbound

This paper cites Self-Knowledge Guided Retrieval Augmentation for Large Language Models.

Enterprise Large Language Model Evaluation Benchmark Self-Knowledge Guided Retrieval Augmentation for Large Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.287298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eee59b13-8eee-4afb-aaac-d1d8f7ddc305 · outbound

This paper cites Making Retrieval-Augmented Language Models Robust to Irrelevant Context.

Enterprise Large Language Model Evaluation Benchmark Making Retrieval-Augmented Language Models Robust to Irrelevant Context

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.381084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78d826a8-66a8-442f-9236-448efac74d03 · outbound

This paper cites Search Augmented Instruction Learning,.

Enterprise Large Language Model Evaluation Benchmark Search Augmented Instruction Learning,

Reference 51

Resolution: verified exact; doi, observed 2026-08-06T22:56:38.781049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d65dc77f-5b7b-46b8-9715-4b0593eafdd6 · outbound

This paper cites Corrective Retrieval Augmented Generation.

Enterprise Large Language Model Evaluation Benchmark Corrective Retrieval Augmented Generation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.594451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fdc0035-c199-4ab5-a018-885e7c3b3cd1 · outbound

This paper cites Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge.

Enterprise Large Language Model Evaluation Benchmark Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.674305Z

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Unavailable: canonical work link unavailable.

Observation 506c000c-7813-4bb1-afb3-7e68efabdd04 · outbound

This paper cites LLM-as-a-Judge: Rethinking Model-Based Evaluations in Text Generation.

Enterprise Large Language Model Evaluation Benchmark LLM-as-a-Judge: Rethinking Model-Based Evaluations in Text Generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.783848Z

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Observation 6b79e00d-3d69-47dd-a684-e6f215c232dd · outbound

This paper cites EvaluatingtheEffectivenessofLLM-Evaluators(akaLLM-as-Judge),.

Enterprise Large Language Model Evaluation Benchmark EvaluatingtheEffectivenessofLLM-Evaluators(akaLLM-as-Judge),

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.892126Z

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Observation 2adcbb2b-ad9c-4e78-9a31-c5ba286a14cf · outbound

This paper cites From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge,.

Enterprise Large Language Model Evaluation Benchmark From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:34.970117Z

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Observation 2e9102f2-ea1c-4a32-b45b-7953a01a4fe7 · outbound

This paper cites Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges.

Enterprise Large Language Model Evaluation Benchmark Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.063138Z

Source-reported events for the cited work

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Observation bb5690b6-7ff5-447d-a462-7b390e3e9370 · outbound

This paper cites PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization.

Enterprise Large Language Model Evaluation Benchmark PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.136253Z

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Observation c167b644-fae9-4b4e-92d4-a899c6265f0a · outbound

This paper cites Evaluating Large Language Models at Evaluating Instruction Following.

Enterprise Large Language Model Evaluation Benchmark Evaluating Large Language Models at Evaluating Instruction Following

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.224892Z

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Observation 2912892d-3c8e-4ef7-ae87-74764814edad · outbound

This paper cites Large Language Models are not Fair Evaluators.

Enterprise Large Language Model Evaluation Benchmark Large Language Models are not Fair Evaluators

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.306523Z

Source-reported events for the cited work

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Observation 830957ef-80a0-49c4-91f6-9dcb9d56bcfd · outbound

This paper cites RevisEval: Improving LLM-as-a-Judge via Response-Adapted References.

Enterprise Large Language Model Evaluation Benchmark RevisEval: Improving LLM-as-a-Judge via Response-Adapted References

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.381259Z

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Observation 585fe516-60ce-4817-84f5-f3f725e0c0a8 · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

Enterprise Large Language Model Evaluation Benchmark G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.487517Z

Source-reported events for the cited work

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Observation 5773381e-02e9-499a-b183-8c697cc39ff6 · outbound

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

Enterprise Large Language Model Evaluation Benchmark Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.554583Z

Source-reported events for the cited work

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Observation 4db0726c-e272-4c73-8765-36a3e1716803 · outbound

This paper cites A Survey on Evaluation of Large Language Models.

Enterprise Large Language Model Evaluation Benchmark A Survey on Evaluation of Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.666171Z

Source-reported events for the cited work

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Observation 38e4ebf3-57ab-4589-864d-4d7ac14a32d5 · outbound

This paper cites A Revision of Bloom’s Taxonomy: An Overview,.

Enterprise Large Language Model Evaluation Benchmark A Revision of Bloom’s Taxonomy: An Overview,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.747231Z

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Observation 72f2525d-010e-42d6-ae0a-33d141fcea04 · outbound

This paper cites Bloom’s Taxonomy,.

Enterprise Large Language Model Evaluation Benchmark Bloom’s Taxonomy,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.835468Z

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Observation 86e53d7c-5187-4191-b5ce-34ccb0d64f6b · outbound

This paper cites Confluence.

Enterprise Large Language Model Evaluation Benchmark Confluence

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.915905Z

Source-reported events for the cited work

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Observation 46327f36-fe8a-466c-8d28-e594c65326f8 · outbound

This paper cites an unresolved cited work.

Enterprise Large Language Model Evaluation Benchmark Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:35.964164Z

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Observation 1d2dbb9d-74a1-44c4-80ac-6d1e777b1a40 · outbound

This paper cites Paraphrase Mining — Sentence Transformers documentation.

Enterprise Large Language Model Evaluation Benchmark Paraphrase Mining — Sentence Transformers documentation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.038525Z

Source-reported events for the cited work

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Observation 18b27fb4-82a0-4593-b28e-af65f5e1d480 · outbound

This paper cites GPT-4 Technical Report.

Enterprise Large Language Model Evaluation Benchmark GPT-4 Technical Report

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.198741Z

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Observation 089737ed-e306-4e46-a3ca-cbaf44920980 · outbound

This paper cites an unresolved cited work.

Enterprise Large Language Model Evaluation Benchmark Unresolved cited work

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.250422Z

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Observation 9ad23cb7-dbf2-4129-aa3e-5d74544709f0 · outbound

This paper cites LLMEvaluationMetrics:TheUltimateLLMEvaluationGuide-ConfidentAI.

Enterprise Large Language Model Evaluation Benchmark LLMEvaluationMetrics:TheUltimateLLMEvaluationGuide-ConfidentAI

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.315106Z

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Observation cf5c50a2-fd44-403a-b187-7191d155b612 · outbound

This paper cites Bleu: a Method for Automatic Evaluation of Machine Translation,.

Enterprise Large Language Model Evaluation Benchmark Bleu: a Method for Automatic Evaluation of Machine Translation,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.395797Z

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Observation b630f755-e53b-4170-92bd-7e9b50a473a6 · outbound

This paper cites ROUGE:APackageforAutomaticEvaluationofSummaries,.

Enterprise Large Language Model Evaluation Benchmark ROUGE:APackageforAutomaticEvaluationofSummaries,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.493280Z

Source-reported events for the cited work

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Observation 8c5a74c5-a64b-458c-b74e-ff1663f8583a · outbound

This paper cites BLEU is Not Suitable for the Evaluation of Text Simplification,.

Enterprise Large Language Model Evaluation Benchmark BLEU is Not Suitable for the Evaluation of Text Simplification,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.620781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a80553d-6cf4-42e1-b187-e7343f5001b0 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Enterprise Large Language Model Evaluation Benchmark BERTScore: Evaluating Text Generation with BERT

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.720000Z

Source-reported events for the cited work

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Observation 684f0aa2-8018-4b07-b488-696038a83d3a · outbound

This paper cites BARTScore: Evaluating Generated Text as Text Generation.

Enterprise Large Language Model Evaluation Benchmark BARTScore: Evaluating Generated Text as Text Generation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.789128Z

Source-reported events for the cited work

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Observation e44ed20f-142a-4064-93a1-b9575306b251 · outbound

This paper cites LLM-based NLG Evaluation: Current Status and Challenges.

Enterprise Large Language Model Evaluation Benchmark LLM-based NLG Evaluation: Current Status and Challenges

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.905484Z

Source-reported events for the cited work

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Observation 8f5e9a53-28fc-494a-b246-5746ac0c8b23 · outbound

This paper cites ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning.

Enterprise Large Language Model Evaluation Benchmark ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.982426Z

Source-reported events for the cited work

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Observation f4e1cc66-fff6-4764-9e7c-c52b7b34ad10 · outbound

This paper cites ChatGPT Outperforms Crowd-Workers for Text- Annotation Tasks,.

Enterprise Large Language Model Evaluation Benchmark ChatGPT Outperforms Crowd-Workers for Text- Annotation Tasks,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:37.051830Z

Source-reported events for the cited work

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Observation 11f977b2-5018-4b10-ac06-6977040725fa · outbound

This paper cites ChatGPT vs. Crowdsourcing vs. Experts: Annotating Open-Domain Conversations with Speech Functions,.

Enterprise Large Language Model Evaluation Benchmark ChatGPT vs. Crowdsourcing vs. Experts: Annotating Open-Domain Conversations with Speech Functions,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:37.176977Z

Source-reported events for the cited work

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Observation c4a18303-5f35-43f6-b77c-6932dd65f3fd · outbound

This paper cites ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness.

Enterprise Large Language Model Evaluation Benchmark ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:56:38.425927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aa05dce6-174c-49f4-b14b-12cfb2f233ba · outbound

This paper cites 30, 2024.

Enterprise Large Language Model Evaluation Benchmark 30, 2024

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:37.367742Z

Source-reported events for the cited work

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Observation 527bb0bd-efe9-4575-9c3f-37f1351c591c · outbound

This paper cites Toxicity.

Enterprise Large Language Model Evaluation Benchmark Toxicity

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:41.053684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c325eaa4-213a-464d-861e-286a8aebfbfc · outbound

This paper cites 29, 2024.

Enterprise Large Language Model Evaluation Benchmark 29, 2024

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:40.828319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b11d65e7-f995-402c-8ecc-f316cf02f38a · outbound

This paper cites Hallucination.

Enterprise Large Language Model Evaluation Benchmark Hallucination

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:40.638498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b80432e5-4c9f-4d81-93ac-861219ef2bbe · outbound

This paper cites Spearman’s rank correlation coefficient,.

Enterprise Large Language Model Evaluation Benchmark Spearman’s rank correlation coefficient,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:40.486401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 68a7b921-f1ae-4c99-9c67-dec9f938cef5 · outbound

This paper cites Llama 3.2: Revolutionizing edge AI and vision with open, customizable models,.

Enterprise Large Language Model Evaluation Benchmark Llama 3.2: Revolutionizing edge AI and vision with open, customizable models,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:40.318963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 99149d66-b16d-4746-b82c-d479a7faab87 · outbound

This paper cites LLaMA 3.3,.

Enterprise Large Language Model Evaluation Benchmark LLaMA 3.3,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:40.180796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4985e23-4100-45a8-807b-7053c4c0bbbc · outbound

This paper cites The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation,.

Enterprise Large Language Model Evaluation Benchmark The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:40.029006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 899a27e9-f9fa-47c0-a575-8ab80fa01051 · outbound

This paper cites Qwen2.5 Technical Report.

Enterprise Large Language Model Evaluation Benchmark Qwen2.5 Technical Report

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:38.092131Z

Source-reported events for the cited work

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Observation e8087ad6-47ac-4231-a96a-0f83174f9110 · outbound

This paper cites The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks.

Enterprise Large Language Model Evaluation Benchmark The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:38.165147Z

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Observation 90c5c1cc-d775-477e-a72f-2e845f4c4ce9 · outbound

This paper cites an unresolved cited work.

Enterprise Large Language Model Evaluation Benchmark Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:56:39.900943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 79aebb99-6b79-4ce5-a16c-b80d5c7d3f40 · outbound

This paper cites This method has been incorporated into the DeepEval package [72].

Enterprise Large Language Model Evaluation Benchmark This method has been incorporated into the DeepEval package [72]

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:56:39.775138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 24b3ca47-ad68-4b1e-82ab-f56a432a0b7e · outbound

This paper cites Available: https://sbert.net/examples/applications/paraphrase-mining/README.html.

Enterprise Large Language Model Evaluation Benchmark Available: https://sbert.net/examples/applications/paraphrase-mining/README.html

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:36.111401Z

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Pith citing papers

Observation a25457eb-cdac-441f-9f2e-0abbe541a5f3 · inbound

Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers cites this paper.

Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers Enterprise Large Language Model Evaluation Benchmark

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:35:22.448174Z

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Observation a7252636-13b0-4af4-bb2e-54afe157cff0 · inbound

EvalLoop: A Methodology for Evaluation-Driven Iterative Improvement of Business AI Systems cites this paper.

EvalLoop: A Methodology for Evaluation-Driven Iterative Improvement of Business AI Systems Enterprise Large Language Model Evaluation Benchmark

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-11T04:37:48.225034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c4c7a638-5768-48f9-aafa-92c553d67644 · inbound

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning cites this paper.

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning Enterprise Large Language Model Evaluation Benchmark

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T21:31:51.354468Z

Source-reported events for the cited work

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Observation ef1b3020-0499-453e-9b27-624dbdd4423f · inbound

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning cites this paper.

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning Enterprise Large Language Model Evaluation Benchmark

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T00:49:50.800327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.