Paper Citation Record · LEDGER

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels

As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2411.13775.

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

pith.paper-citation-record.v1
2411.13775 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

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measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:11:13.400147Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:11:14.121736Z

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation 9d0efe89-a40c-431e-ac48-ae31a1f1696b · outbound

This paper cites Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine

Reference 1

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This paper cites Document-level machine translation with large language models,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Document-level machine translation with large language models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:00.469453Z

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Observation 63042b39-a464-43cb-b053-733fbb546681 · outbound

This paper cites From LLM to NMT: Advancing Low-Resource Machine Translation with Claude.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels From LLM to NMT: Advancing Low-Resource Machine Translation with Claude

Reference 3

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This paper cites Towards making the most of llm for translation quality estimation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Towards making the most of llm for translation quality estimation,

Reference 4

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Observation 94d8dc8e-2b00-467b-be11-163a05b6a8d9 · outbound

This paper cites Adapting Large Language Models for Document-Level Machine Translation.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Adapting Large Language Models for Document-Level Machine Translation

Reference 5

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Observation 040dfe16-cf74-4f4d-ab1e-ea2f7f7491ce · outbound

This paper cites How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

Reference 6

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This paper cites Towards making the most of chatgpt for machine translation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Towards making the most of chatgpt for machine translation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:00.436179Z

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Observation 4521e6fd-a7a4-4801-a42e-e6e01c5d95b5 · outbound

This paper cites What is the best way for ChatGPT to translate poetry?.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels What is the best way for ChatGPT to translate poetry?

Reference 9

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Observation 40392461-fb42-4707-a20c-a9e2adb02bbc · outbound

This paper cites Revisiting cross- lingual summarization: A corpus-based study and a new benchmark with improved annotation.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Revisiting cross- lingual summarization: A corpus-based study and a new benchmark with improved annotation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:00.404078Z

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Observation 36f26620-a2c8-4401-97bc-bd21a8460b2e · outbound

This paper cites The price of debiasing automatic metrics in natural language evalaution,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels The price of debiasing automatic metrics in natural language evalaution,

Reference 11

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Observation a529aaaa-5a33-48ed-aa01-d5f2195f0b05 · outbound

This paper cites Results of the WMT19 metrics shared task: Segment-level and strong MT systems pose big challenges,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Results of the WMT19 metrics shared task: Segment-level and strong MT systems pose big challenges,

Reference 12

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Observation e541fbc6-452f-4ca9-91c9-d3589c0e7d54 · outbound

This paper cites BLEURT: Learning robust metrics for text generation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels BLEURT: Learning robust metrics for text generation,

Reference 14

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This paper cites Experts, errors, and context: A large- scale study of human evaluation for machine translation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Experts, errors, and context: A large- scale study of human evaluation for machine translation,

Reference 15

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Observation 052a4e68-7256-4c08-a965-1578c85b7550 · outbound

This paper cites CLUE: A Chinese Language Understanding Evaluation Benchmark.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels CLUE: A Chinese Language Understanding Evaluation Benchmark

Reference 16

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Observation 7f49f40d-d55d-4dbf-afc5-3cf55ccf8a29 · outbound

This paper cites Benchmarking llms via uncertainty quantification,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Benchmarking llms via uncertainty quantification,

Reference 17

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Observation 47059008-98e6-42f5-b91d-5d7d1585a89c · outbound

This paper cites Measuring massive multitask language understanding,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Measuring massive multitask language understanding,

Reference 18

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Observation b3e33ce1-657a-4438-9c61-dd64e4ca600f · outbound

This paper cites Revisiting out-of-distribution robust- ness in nlp: Benchmark, analysis, and llms evaluations,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Revisiting out-of-distribution robust- ness in nlp: Benchmark, analysis, and llms evaluations,

Reference 19

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This paper cites Is chatgpt a good translator? yes with gpt-4 as the engine,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Is chatgpt a good translator? yes with gpt-4 as the engine,

Reference 20

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This paper cites Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset

Reference 21

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This paper cites Evaluating large language models for radiology natural language processing,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Evaluating large language models for radiology natural language processing,

Reference 22

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This paper cites News Summarization and Evaluation in the Era of GPT-3.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels News Summarization and Evaluation in the Era of GPT-3

Reference 23

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Observation 99907c1f-f5f4-40a4-88da-88d13fbc5820 · outbound

This paper cites Using gpt-4 to provide tiered, formative code feedback,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Using gpt-4 to provide tiered, formative code feedback,

Reference 24

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Observation 6675944a-8299-45c5-a0c1-173f2a8ab9be · outbound

This paper cites A comparison of human and gpt-4 use of probabilistic phrases in a coordination game,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels A comparison of human and gpt-4 use of probabilistic phrases in a coordination game,

Reference 25

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Observation 09016438-5a3e-4ff5-994e-e6e3a58b9bf9 · outbound

This paper cites Chatgpt and gpt-4 for professional translators: Exploring the potential of large language models in translation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Chatgpt and gpt-4 for professional translators: Exploring the potential of large language models in translation,

Reference 26

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Observation f9a77c46-8bc0-4462-828e-8a16b304319f · outbound

This paper cites Does GPT-4 surpass human performance in linguistic pragmatics?.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Does GPT-4 surpass human performance in linguistic pragmatics?

Reference 27

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Observation 0b1fa082-81c4-4d5a-b0c8-ce7b3fdf81fc · outbound

This paper cites Comparative analysis of gpt-4vision, gpt-4 and open source llms in clinical diag- nostic accuracy: A benchmark against human expertise,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Comparative analysis of gpt-4vision, gpt-4 and open source llms in clinical diag- nostic accuracy: A benchmark against human expertise,

Reference 28

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This paper cites Continuous measurement scales in human evaluation of machine translation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Continuous measurement scales in human evaluation of machine translation,

Reference 29

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This paper cites Findings of the 2021 conference on machine translation (wmt21),.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Findings of the 2021 conference on machine translation (wmt21),

Reference 30

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This paper cites Findings of the 2022 conference on machine translation (wmt22),.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Findings of the 2022 conference on machine translation (wmt22),

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.981751Z

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This paper cites Findings of the 2023 conference on machine translation (wmt23): Llms are here but not quite there yet,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Findings of the 2023 conference on machine translation (wmt23): Llms are here but not quite there yet,

Reference 32

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This paper cites Assessing inter-annotator agreement for translation error annotation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Assessing inter-annotator agreement for translation error annotation,

Reference 33

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This paper cites Quantitative fine-grained human evaluation of machine translation systems: a case study on english to croatian,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Quantitative fine-grained human evaluation of machine translation systems: a case study on english to croatian,

Reference 34

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This paper cites COMET: A neural framework for MT evaluation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels COMET: A neural framework for MT evaluation,

Reference 35

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This paper cites Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.915721Z

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Observation ca1b0cb6-503b-402e-a1cb-f2a5db47431c · outbound

This paper cites Results of WMT23 metrics shared task: Metrics might be guilty but references are not innocent,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Results of WMT23 metrics shared task: Metrics might be guilty but references are not innocent,

Reference 37

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This paper cites Achieving Human Parity on Automatic Chinese to English News Translation.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Achieving Human Parity on Automatic Chinese to English News Translation

Reference 38

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This paper cites aubli, “What’s the difference between professional human and machine translation? a blind multi-language study on domain-specific MT,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels aubli, “What’s the difference between professional human and machine translation? a blind multi-language study on domain-specific MT,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.883055Z

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Observation 82db6a20-27ee-4c56-bab4-4bebadfa6ee3 · outbound

This paper cites Attaining the unattainable? reassessing claims of human parity in neural machine translation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Attaining the unattainable? reassessing claims of human parity in neural machine translation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.866785Z

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Observation 7e3fd4bb-7fc1-4298-a0ec-e95af82c0bea · outbound

This paper cites The suboptimal wmt test sets and its impact on human 12 parity,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels The suboptimal wmt test sets and its impact on human 12 parity,

Reference 41

Resolution: verified exact; raw_fallback, observed 2026-08-12T15:57:59.472395Z

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Observation 76b702ca-ae6b-47da-9c1d-6f2725f1e02b · outbound

This paper cites On" human parity.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels On" human parity

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.851342Z

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Observation 34d3d709-df9e-465d-9e95-1f426ae8fd33 · outbound

This paper cites Assessing human-parity in machine translation on the segment level,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Assessing human-parity in machine translation on the segment level,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.835335Z

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Observation 39592dcf-237c-44b2-b543-72f49a0aed08 · outbound

This paper cites SeamlessM4T: Massively Multilingual & Multimodal Machine Translation.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels SeamlessM4T: Massively Multilingual & Multimodal Machine Translation

Reference 44

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Observation 6b340e10-fbc3-401c-ac2e-0901e82f103c · outbound

This paper cites Calibrate before use: Improving few-shot performance of language models,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Calibrate before use: Improving few-shot performance of language models,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T15:57:59.267659Z

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Observation 73d907f9-783f-4a66-bd08-7cf2a060edad · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.808512Z

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Observation f883a8c5-8272-489e-800f-53b8809e77db · outbound

This paper cites A paradigm shift in machine translation: Boosting translation performance of large language models,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels A paradigm shift in machine translation: Boosting translation performance of large language models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.792365Z

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Observation e9b5c8e7-679d-4625-b7e4-300baf8296ee · outbound

This paper cites Comet: A neural framework for mt evaluation,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Comet: A neural framework for mt evaluation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.775722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fbe27486-29a6-4d6b-9499-dbdc126b2d23 · outbound

This paper cites doccano: Text annotation tool for human,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels doccano: Text annotation tool for human,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.759733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e367a4c2-752a-4c2b-bd0e-c341c8d8d00f · outbound

This paper cites A coefficient of agreement for nominal scales,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels A coefficient of agreement for nominal scales,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T15:57:59.296481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53ce2c5b-636f-40f0-92f0-98f26bd75074 · outbound

This paper cites Validity in content analysis,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Validity in content analysis,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.733467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05f34536-60c7-496e-90dd-73795c35a7ea · outbound

This paper cites Not all countries celebrate thanksgiving: On the cultural dominance in large language models,.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Not all countries celebrate thanksgiving: On the cultural dominance in large language models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:59.716804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23c12a05-1e9e-4938-8d03-15ae2f193816 · outbound

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

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T15:57:59.311066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4edbe24d-32fc-4c82-9288-930eef5be2e2 · outbound

This paper cites TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T15:57:59.316216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83a043c7-7aea-4856-a638-746d09a6a0a8 · outbound

This paper cites (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts.

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T15:57:59.321417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 44a04cea-a814-4844-97a7-edf5517ed247 · inbound

Dual Debiasing for Noisy In-Context Learning for Text Generation cites this paper.

Dual Debiasing for Noisy In-Context Learning for Text Generation Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:11:14.152040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.