Summary of A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations, by Md Tahmid Rahman Laskar et al.
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
by 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
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the inconsistencies in evaluating Large Language Models (LLMs) and proposes a framework for ensuring reliable performance. By systematically reviewing the challenges and limitations in various steps of LLM evaluation, the authors identify key issues causing inconsistent findings and interpretations. The study’s main contribution is providing perspectives and recommendations to ensure reproducible, reliable, and robust evaluations of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can trust what large language models do. These models are very good at doing many things, but we need to make sure they work well in real-life situations. Right now, there are different ways to test these models, which makes it hard to compare results. The authors think about why this is a problem and offer ideas for how to fix it so we can trust the results. |
