---
title: "Evaluation & benchmarks — papers, page 5 — Elamur Research"
description: "Evaluation & benchmarks: 684 papers of the Elamur Research library · page 5 of 8 · 326–425, the most cited first."
canonical: "https://research.elamur.ai/free/en/topics/evaluation-benchmarks/page/5/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/topics/evaluation-benchmarks/page/5/)

[home](https://md.research.elamur.ai/free/en/) / [topics](https://md.research.elamur.ai/free/en/topics/) / [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) / page 5

# 📏 Evaluation & benchmarks

684 papers · page 5 of 8 · 326–425 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

How systems are measured: benchmarks, metrics, human judgements and what the numbers mean.

## Papers (684)

[← prev](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/4/) [1](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) … [4](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/4/) **5** [6](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/6/) [7](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/7/) [8](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/8/) [next →](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/6/)

326. [**MEANT 2.0: Accurate semantic MT evaluation for any output language**Chi-kiu Lo*2017 · 82 citations*](https://md.research.elamur.ai/free/en/papers/lo-2017-meant-2-0-accurate-semantic/)
327. **[Findings of the WMT 2021 Shared Task on Quality Estimation](https://md.research.elamur.ai/free/en/papers/specia-2021-findings-wmt-2021-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Lucia Specia, Frédéric Blain, Marina Fomicheva, et al.*2021 · 80 citations*
328. **[Generating Benchmarks for Factuality Evaluation of Language Models](https://md.research.elamur.ai/free/en/papers/muhlgay-2024-generating-benchmarks-factuality-evaluation-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Dor Muhlgay, Ori Ram, Inbal Magar, et al.*2024 · 79 citations*
329. [**Formalism-Independent Parser Evaluation with CCG and DepBank**Stephen Clark, James Curran*2007 · 79 citations*](https://md.research.elamur.ai/free/en/papers/clark-2007-formalism-independent-parser-evaluation-ccg/)
330. **[KoBBQ: Korean Bias Benchmark for Question Answering](https://md.research.elamur.ai/free/en/papers/jin-2024-kobbq-korean-bias-benchmark-question/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiho Jin, Jiseon Kim, Nayeon Lee, et al.*2024 · 77 citations*
331. [**Evaluating prose style transfer with the Bible**Keith Carlson, Allen Riddell, Daniel Rockmore*2017 · 77 citations*](https://md.research.elamur.ai/free/en/papers/carlson-2017-evaluating-prose-style-transfer-bible/)
332. **[A survey on Recognizing Textual Entailment as an NLP Evaluation](https://md.research.elamur.ai/free/en/papers/poliak-2020-survey-recognizing-textual-entailment-as/) [Workshop on Evaluation and Comparison of NLP Systems](https://md.research.elamur.ai/free/en/venues/eval4nlp/)**Adam Poliak*2020 · 76 citations*
333. [**A Dependency Perspective on RST Discourse Parsing and Evaluation**Mathieu Morey, Philippe Muller, Nicholas Asher*2018 · 76 citations*](https://md.research.elamur.ai/free/en/papers/morey-2018-dependency-perspective-rst-discourse-parsing/)
334. **[AudioBench: A Universal Benchmark for Audio Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-audiobench-universal-benchmark-audio-large/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Xunlong Zou, Geyu Lin, et al.*2025 · 75 citations*
335. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
336. [**Extracting and evaluating general world knowledge from the Brown Corpus**Lenhart Schubert, Matthew Tong*2003 · 75 citations*](https://md.research.elamur.ai/free/en/papers/schubert-2003-extracting-evaluating-general-world-knowledge/)
337. **[MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/xuan-2025-mmlu-prox-multilingual-benchmark-advanced/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weihao Xuan, Rui Yang, Heli Qi, et al.*2025 · 74 citations*
338. **[Findings of the WMT25 General Machine Translation Shared Task: Time to Stop Evaluating on Easy Test Sets](https://md.research.elamur.ai/free/en/papers/kocmi-2025-findings-wmt25-general-machine-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Tom Kocmi, Ekaterina Artemova, Eleftherios Avramidis, et al.*2025 · 73 citations*
339. **[Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks](https://md.research.elamur.ai/free/en/papers/zhou-2024-is-llm-reliable-reviewer-comprehensive/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ruiyang Zhou, Lu Chen, Kai Yu*2024 · 73 citations*
340. [**OR-Bench: An Over-Refusal Benchmark for Large Language Models arXiv:2405.20947**Justin Cui, Wei-Lin Chiang, Ion Stoica, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/cui-2024-bench-over-refusal-benchmark-large/)
341. **[ScandEval: A Benchmark for Scandinavian Natural Language Processing](https://md.research.elamur.ai/free/en/papers/nielsen-2023-scandeval-benchmark-scandinavian-natural-language/) [Nordic Conference on Computational Linguistics (NoDaLiDa)](https://md.research.elamur.ai/free/en/venues/nodalida/)**Dan Nielsen*2023 · 73 citations*
342. **[CLiMP: A Benchmark for Chinese Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/xiang-2021-climp-benchmark-chinese-language-model/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Beilei Xiang, Changbing Yang, Yu Li, et al.*2021 · 73 citations*
343. **[Evaluating BERT for natural language inference: A case study on the CommitmentBank](https://md.research.elamur.ai/free/en/papers/jiang-2019-evaluating-bert-natural-language-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nanjiang Jiang, Marie-Catherine de Marneffe*2019 · 72 citations*
344. [**CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark arXiv:2106.08087**Ningyu Zhang, Mosha Chen, Zhen Bi, et al.*2021 · 71 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2021-cblue-chinese-biomedical-language/)
345. [**BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval arXiv:2407.12883**Hongjin Su, Howard Yen, Mengzhou Xia, et al.*2024 · 70 citations*](https://md.research.elamur.ai/free/en/papers/su-2024-bright-realistic-challenging-benchmark-reasoning/)
346. **[MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://md.research.elamur.ai/free/en/papers/li-2024-mediq-question-asking-llms-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, et al.*2024 · 70 citations*
347. [**StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts**Zhengxiang Shi, Qiang Zhang, Aldo Lipani*2022 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-stepgame-new-benchmark-robust-multi/)
348. **[IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models](https://md.research.elamur.ai/free/en/papers/adelani-2025-irokobench-new-benchmark-african-languages/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, et al.*2025 · 69 citations*
349. [**Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? arXiv:2309.07462**Rishav Hada, Varun Gumma, Adrian de Wynter, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/hada-2023-large-language-model-based-evaluators/)
350. **[Findings of the WMT 2017 Biomedical Translation Shared Task](https://md.research.elamur.ai/free/en/papers/yepes-2017-findings-wmt-2017-biomedical-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Antonio Jimeno Yepes, Aurélie Névéol, Mariana Neves, et al.*2017 · 69 citations*
351. **[AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs](https://md.research.elamur.ai/free/en/papers/mousi-2025-aradice-benchmarks-dialectal-cultural-capabilities/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Basel Mousi, Nadir Durrani, Fatema Ahmad, et al.*2025 · 68 citations*
352. **[Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric](https://md.research.elamur.ai/free/en/papers/vernikos-2022-embarrassingly-easy-document-level-mt/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Giorgos Vernikos, Brian Thompson, Prashant Mathur, et al.*2022 · 68 citations*
353. **[ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities](https://md.research.elamur.ai/free/en/papers/lu-2025-toolsandbox-stateful-conversational-interactive-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiarui Lu, Thomas Holleis, Yizhe Zhang, et al.*2025 · 67 citations*
354. **[MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/kwan-2024-mt-eval-multi-turn-capabilities/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, et al.*2024 · 66 citations*
355. **[Findings of the WMT 2018 Shared Task on Automatic Post-Editing](https://md.research.elamur.ai/free/en/papers/chatterjee-2018-findings-wmt-2018-shared-task/) [Conference on Machine Translation: Shared Task Papers](https://md.research.elamur.ai/free/en/venues/conference-on-machine-translation-shared-task-papers/)**Rajen Chatterjee, Matteo Negri, Raphael Rubino, et al.*2018 · 66 citations*
356. **[Evaluating Topic Coherence Using Distributional Semantics](https://md.research.elamur.ai/free/en/papers/aletras-2013-evaluating-topic-coherence-distributional-semantics/) [International Conference on Computational Semantics – Long Papers](https://md.research.elamur.ai/free/en/venues/international-conference-on-computational-semantics-long-papers/)**Nikolaos Aletras, Mark Stevenson*2013 · 64 citations*
357. **[A Fine-Grained Analysis of BERTScore](https://md.research.elamur.ai/free/en/papers/hanna-2021-fine-grained-analysis-bertscore/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Michael Hanna, Ondřej Bojar*2021 · 62 citations*
358. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/)
359. **[BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models](https://md.research.elamur.ai/free/en/papers/dong-2024-bamboo-comprehensive-benchmark-evaluating-long/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Zican Dong, Tianyi Tang, Junyi Li, et al.*2024 · 59 citations*
360. [**HUME: Human UCCA-Based Evaluation of Machine Translation**Alexandra Birch, Omri Abend, Ondřej Bojar, et al.*2016 · 58 citations*](https://md.research.elamur.ai/free/en/papers/birch-2016-hume-human-ucca-based-evaluation/)
361. **[Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task](https://md.research.elamur.ai/free/en/papers/sellam-2020-learning-evaluate-translation-beyond-english/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Thibault Sellam, Amy Pu, Hyung Won Chung, et al.*2020 · 55 citations*
362. [**CMB: A Comprehensive Medical Benchmark in Chinese arXiv:2308.08833**Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cmb-comprehensive-medical-benchmark/)
363. [**Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples**Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, et al.*2020 · 54 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2020-evaluating-enhancing-robustness-neural-network/)
364. **[Findings of the WMT 2019 Biomedical Translation Shared Task: Evaluation for MEDLINE Abstracts and Biomedical Terminologies](https://md.research.elamur.ai/free/en/papers/bawden-2019-findings-wmt-2019-biomedical-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Rachel Bawden, Kevin Bretonnel Cohen, Cristian Grozea, et al.*2019 · 54 citations*
365. [**Parser Evaluation over Local and Non-Local Deep Dependencies in a Large Corpus**Emily M. Bender, Dan Flickinger, Stephan Oepen, et al.*2011 · 53 citations*](https://md.research.elamur.ai/free/en/papers/bender-2011-parser-evaluation-over-local-non/)
366. [**Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies**Gosse Bouma, Djamé Seddah, Daniel Zeman*2020 · 52 citations*](https://md.research.elamur.ai/free/en/papers/bouma-2020-overview-iwpt-2020-shared-task/)
367. [**Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction EMNLP-IJCNLP 2019 (Hong Kong)**Yova Kementchedjhieva, Mareike Hartmann, Anders Sgaard*2019 · 52 citations*](https://md.research.elamur.ai/free/en/papers/kementchedjhieva-2019-lost-in-evaluation/)
368. **[Evaluation Tool for Rule-based Anaphora Resolution Methods](https://md.research.elamur.ai/free/en/papers/barbu-2001-evaluation-tool-rule-based-anaphora/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Catalina Barbu, Ruslan Mitkov*2001 · 52 citations*
369. **[XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://md.research.elamur.ai/free/en/papers/khan-2024-xcodeeval-execution-based-large-scale/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.*2024 · 51 citations*
370. [**Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR**Juri Opitz, Anette Frank*2021 · 51 citations*](https://md.research.elamur.ai/free/en/papers/opitz-2021-decomposable-metric-explainable-evaluation-text/)
371. [**Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer**Eleftheria Briakou, Sweta Agrawal, Joel Tetreault, et al.*2021 · 49 citations*](https://md.research.elamur.ai/free/en/papers/briakou-2021-evaluating-evaluation-metrics-style-transfer/)
372. **[Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems](https://md.research.elamur.ai/free/en/papers/asano-2017-reference-based-metrics-can-be/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Hiroki Asano, Tomoya Mizumoto, Kentaro Inui*2017 · 49 citations*
373. **[How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech](https://md.research.elamur.ai/free/en/papers/yedetore-2023-how-poor-is-stimulus-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aditya Yedetore, Tal Linzen, Robert Frank, et al.*2023 · 48 citations*
374. [**Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages arXiv:2112.09301**Thomas Mandl, Sandip Modha, Gautam Kishore Shahi, et al.*2021 · 48 citations*](https://md.research.elamur.ai/free/en/papers/mandl-2021-overview-hasoc-subtrack-fire-2021/)
375. [**Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity**Juri Opitz, Angel Daza, Anette Frank*2021 · 47 citations*](https://md.research.elamur.ai/free/en/papers/opitz-2021-weisfeiler-leman-bamboo-novel-amr/)
376. [**Evaluating Dependency Parsing: Robust and Heuristics-Free Cross-Annotation Evaluation**Reut Tsarfaty, Joakim Nivre, Evelina Andersson*2011 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tsarfaty-2011-evaluating-dependency-parsing-robust-heuristics/)
377. **[Taking MT Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments](https://md.research.elamur.ai/free/en/papers/fomicheva-2019-taking-mt-evaluation-metrics-extremes/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Marina Fomicheva, Lucia Specia*2019 · 45 citations*
378. **[Findings of the WMT 2023 Shared Task on Low-Resource Indic Language Translation](https://md.research.elamur.ai/free/en/papers/pal-2023-findings-wmt-2023-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Santanu Pal, Partha Pakray, Sahinur Rahman Laskar, et al.*2023 · 41 citations*
379. **[Evaluating FrameNet-style semantic parsing: the role of coverage gaps in FrameNet](https://md.research.elamur.ai/free/en/papers/palmer-2010-evaluating-framenet-style-semantic-parsing/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Alexis Palmer, Caroline Sporleder*2010 · 41 citations*
380. **[Evaluating a Focus-Based Approach to Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/azzam-1998-evaluating-focus-based-approach-anaphora/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Saliha Azzam, Kevin Humphreys, Robert Gaizauskas*1998 · 41 citations*
381. **[Automatic Evaluation and Analysis of Idioms in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/baziotis-2023-automatic-evaluation-analysis-idioms-neural/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Christos Baziotis, Prashant Mathur, Eva Hasler*2023 · 40 citations*
382. [**xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval arXiv:2303.03004**Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/khan-2023-xcodeeval-large-scale-multilingual-multitask/)
383. **[Discourse Structure in Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/joty-2017-discourse-structure-machine-translation-evaluation/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Shafiq Joty, Francisco Guzmán, Lluís Màrquez, et al.*2017 · 40 citations*
384. **[Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)](https://md.research.elamur.ai/free/en/papers/muller-2023-findings-second-wmt-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Mathias Müller, Malihe Alikhani, Eleftherios Avramidis, et al.*2023 · 39 citations*
385. [**ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset arXiv:2011.00578**Basma Alharbi, Hind Alamro, Manal Alshehri, et al.*2020 · 39 citations*](https://md.research.elamur.ai/free/en/papers/alharbi-2020-asad-twitter-based-benchmark-arabic/)
386. **[Linguistic Evaluation of German-English Machine Translation Using a Test Suite](https://md.research.elamur.ai/free/en/papers/avramidis-2019-linguistic-evaluation-german-english-machine/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Eleftherios Avramidis, Vivien Macketanz, Ursula Strohriegel, et al.*2019 · 38 citations*
387. **[Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis](https://md.research.elamur.ai/free/en/papers/nassif-2021-empirical-evaluation-shallow-deep-learning/) [ACM Transactions on Asian and Low-Resource Language Information Processing](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-asian-and-low-resource-language-information-processing/)**Ali Bou Nassif, Abdollah Masoud Darya, Ashraf Elnagar*2021 · 34 citations*
388. **[FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://md.research.elamur.ai/free/en/papers/riley-2023-frmt-benchmark-few-shot-region/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Parker Riley, Timothy Dozat, Jan A. Botha, et al.*2023 · 33 citations*
389. **[Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains](https://md.research.elamur.ai/free/en/papers/zouhar-2024-fine-tuned-machine-translation-metrics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vilém Zouhar, Shuoyang Ding, Anna Currey, et al.*2024 · 32 citations*
390. **[MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem](https://md.research.elamur.ai/free/en/papers/perrella-2022-matese-machine-translation-evaluation-as/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, et al.*2022 · 32 citations*
391. **[Realistic Evaluation Principles for Cross-document Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cattan-2021-realistic-evaluation-principles-cross-document/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Arie Cattan, Alon Eirew, Gabriel Stanovsky, et al.*2021 · 31 citations*
392. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
393. [**MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods arXiv:2309.10966**Mara Finkelstein; Subhajit Naskar; Mehdi Mirzazadeh; Apurva Shah;, Markus Freitag*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/finkelstein-2023-mbr-qe-finetuning-training-time/)
394. **[Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment](https://md.research.elamur.ai/free/en/papers/jiang-2019-do-you-know-that-florence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nanjiang Jiang, Marie-Catherine de Marneffe*2019 · 30 citations*
395. **[Revisiting the Evaluation for Cross Document Event Coreference](https://md.research.elamur.ai/free/en/papers/upadhyay-2016-revisiting-evaluation-cross-document-event/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Shyam Upadhyay, Nitish Gupta, Christos Christodoulopoulos, et al.*2016 · 30 citations*
396. [**ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics**Chantal Amrhein, Nikita Moghe, Liane Guillou*2022 · 29 citations*](https://md.research.elamur.ai/free/en/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/)
397. **[Findings of the WMT 2020 Shared Task on Chat Translation](https://md.research.elamur.ai/free/en/papers/farajian-2020-findings-wmt-2020-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**M. Amin Farajian, António V. Lopes, André F. T. Martins, et al.*2020 · 29 citations*
398. **[Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets](https://md.research.elamur.ai/free/en/papers/moghe-2025-machine-translation-meta-evaluation-through/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Nikita Moghe, Arnisa Fazla, Chantal Amrhein, et al.*2025 · 28 citations*
399. [**SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference**Martin Schmitt, Hinrich Schütze*2019 · 28 citations*](https://md.research.elamur.ai/free/en/papers/schmitt-2019-sherliic-typed-event-focused-lexical/)
400. [**RED: A Reference Dependency Based MT Evaluation Metric**Hui Yu, Xiaofeng Wu, Jun Xie, et al.*2014 · 28 citations*](https://md.research.elamur.ai/free/en/papers/yu-2014-red-reference-dependency-based-mt/)
401. **[Findings of the Second Shared Task on Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/zabokrtsky-2023-findings-second-shared-task-multilingual/) [Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC)](https://md.research.elamur.ai/free/en/venues/crac/)**Zdeněk Žabokrtský, Miloslav Konopik, Anna Nedoluzhko, et al.*2023 · 27 citations*
402. [**The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers**Murhaf Fares, Stephan Oepen, Lilja Øvrelid, et al.*2018 · 27 citations*](https://md.research.elamur.ai/free/en/papers/fares-2018-2018-shared-task-extrinsic-parser/)
403. **[Description, Evaluation and Stance: Exploring the Forms and Functions of Speech Descriptors in Early Modern English](https://md.research.elamur.ai/free/en/papers/grund-2017-description-evaluation-stance-exploring-forms/) [Nordic Journal of English Studies](https://md.research.elamur.ai/free/en/venues/nordic-journal-of-english-studies/)**Peter J. Grund*2017 · 27 citations*
404. [**The Multidimensional Quality Metrics (MQM) Framework: a new framework for translation quality assessment**Valerie Mariana, Troy Cox, Alan Melby*2015 · 27 citations*](https://md.research.elamur.ai/free/en/papers/mariana-2015-multidimensional-quality-metrics-mqm-framework/)
405. [**How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark**Minglai Yang, Ethan Huang, Liang Zhang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-llm-reasoning-distracted-irrelevant-context/)
406. **[M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/feng-2025-m-mad-multidimensional-multi-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaopeng Feng, Jiayuan Su, Jiamei Zheng, et al.*2025 · 24 citations*
407. **[Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!](https://md.research.elamur.ai/free/en/papers/perrella-2024-guardians-machine-translation-meta-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, et al.*2024 · 24 citations*
408. **[Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation](https://md.research.elamur.ai/free/en/papers/truong-2022-not-another-negation-benchmark-nan/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Hung Thinh Truong, Yulia Otmakhova, Timothy Baldwin, et al.*2022 · 24 citations*
409. [**A Human Evaluation of AMR-to-English Generation Systems**Emma Manning, Shira Wein, Nathan Schneider*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/manning-2020-human-evaluation-amr-english-generation/)
410. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
411. **[A New Amharic Speech Emotion Dataset and Classification Benchmark](https://md.research.elamur.ai/free/en/papers/retta-2022-new-amharic-speech-emotion-dataset/) [ACM Transactions on Asian and Low-Resource Language Information Processing](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-asian-and-low-resource-language-information-processing/)**Ephrem A. Retta, Eiad Almekhlafi, Richard Sutcliffe, et al.*2022 · 23 citations*
412. **[Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation](https://md.research.elamur.ai/free/en/papers/gotou-2020-taking-correction-difficulty-into-account/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Takumi Gotou, Ryo Nagata, Masato Mita, et al.*2020 · 23 citations*
413. [**Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims arXiv:2109.15118 · CLEF-2019**Tamer Elsayed, Preslav Nakov, Alberto Barron-Cedeno, et al.*2019 · 23 citations*](https://md.research.elamur.ai/free/en/papers/elsayed-2021-overview-clef-2019-checkthat-automatic/)
414. **[Coreference Resolution Evaluation for Higher Level Applications](https://md.research.elamur.ai/free/en/papers/tuggener-2014-coreference-resolution-evaluation-higher-level/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Don Tuggener*2014 · 23 citations*
415. **[Evaluation of Discourse Relation Annotation in the Hindi Discourse Relation Bank](https://md.research.elamur.ai/free/en/papers/kolachina-2012-evaluation-discourse-relation-annotation-hindi/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Sudheer Kolachina, Rashmi Prasad, Dipti Misra Sharma, et al.*2012 · 23 citations*
416. **[Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models](https://md.research.elamur.ai/free/en/papers/kim-2024-developing-pragmatic-benchmark-assessing-korean/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yeeun Kim, Youngrok Choi, Eunkyung Choi, et al.*2024 · 22 citations*
417. [**Singletons and Coreference Resolution Evaluation**Sandra Kübler, Desislava Zhekova*2011 · 22 citations*](https://md.research.elamur.ai/free/en/papers/kubler-2011-singletons-coreference-resolution-evaluation/)
418. **[A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News](https://md.research.elamur.ai/free/en/papers/poot-2020-benchmark-rule-based-neural-coreference/) [Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC)](https://md.research.elamur.ai/free/en/venues/crac/)**Corbèn Poot, Andreas van Cranenburgh*2020 · 21 citations*
419. [**BLiMP: The Benchmark of Linguistic Minimal Pairs for English**Alex Warstadt, Alicia Parrish, Haokun Liu, et al.*2020 · 21 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2020-blimp-benchmark-linguistic-minimal-pairs/)
420. **[Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/teodorescu-2023-evaluating-emotion-arcs-across-languages/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniela Teodorescu, Saif Mohammad*2023 · 20 citations*
421. [**KoBE: Knowledge-Based Machine Translation Evaluation**Zorik Gekhman, Roee Aharoni, Genady Beryozkin, et al.*2020 · 20 citations*](https://md.research.elamur.ai/free/en/papers/gekhman-2020-kobe-knowledge-based-machine-translation/)
422. [**Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection**Nafise Sadat Moosavi, Leo Born, Massimo Poesio, et al.*2019 · 20 citations*](https://md.research.elamur.ai/free/en/papers/moosavi-2019-automatically-extracted-minimum-spans-disentangle/)
423. [**Evaluating a Statistical CCG Parser on Wikipedia**Matthew Honnibal, Joel Nothman, James R. Curran*2009 · 19 citations*](https://md.research.elamur.ai/free/en/papers/honnibal-2009-evaluating-statistical-ccg-parser-wikipedia/)
424. **[Benchmarking LLMs for Translating Classical Chinese Poetry: Evaluating Adequacy, Fluency, and Elegance](https://md.research.elamur.ai/free/en/papers/chen-2025-benchmarking-llms-translating-classical-chinese/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andong Chen, Lianzhang Lou, Kehai Chen, et al.*2025 · 18 citations*
425. [**CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks arXiv:2409.12623**Zhaozhi Qian, Faroq Altam, Muhammad Alqurishi, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/qian-2024-cameleval-advancing-culturally-aligned-arabic/)

[← prev](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/4/) [1](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) … [4](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/4/) **5** [6](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/6/) [7](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/7/) [8](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/8/) [next →](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/page/6/)