| Challenge: | Large Language Models (LLMs) undergo extensive evaluation against various benchmarks collected in established leaderboards to assess their performance across multiple tasks. |
| Approach: | They propose a new evaluation methodology to test LLMs' sentence generation abilities under specific linguistic constraints. |
| Outcome: | The proposed evaluation methodology is based on the 'linguistic profiling' approach and is not intended to be a task-oriented evaluation. |
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
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Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)
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Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma
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Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
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Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. |
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Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness (2024.findings-acl)
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| Challenge: | Existing evaluation approaches for large language models (LLMs) rely on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. |
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Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024.lrec-main)
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Yixin Liu, Alexander Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, Arman Cohan
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Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)
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| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
| Approach: | This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods. |
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Evaluating the Deductive Competence of Large Language Models (2024.naacl-long)
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| Challenge: | Existing large language models have limited abilities to solve deductive reasoning problems . performance differences between conditions do not improve overall performance . |
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Chinese Morpheme-informed Evaluation of Large Language Models (2024.lrec-main)
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| Challenge: | Existing evaluations of large language models focused on the perspective of various tasks or abilities. |
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