Summary of Law Of the Weakest Link: Cross Capabilities Of Large Language Models, by Ming Zhong et al.
Law of the Weakest Link: Cross Capabilities of Large Language Models
by Ming Zhong, Aston Zhang, Xuewei Wang, Rui Hou, Wenhan Xiong, Chenguang Zhu, Zhengxing Chen, Liang Tan, Chloe Bi, Mike Lewis, Sravya Popuri, Sharan Narang, Melanie Kambadur, Dhruv Mahajan, Sergey Edunov, Jiawei Han, Laurens van der Maaten
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel framework is introduced to investigate the intersection of multiple Large Language Model (LLM) capabilities required for real-world tasks. The paper defines seven individual capabilities and pairs them to form seven common cross capabilities, each supported by a manually constructed taxonomy. A benchmark called CrossEval is proposed, comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, expert annotators assess model responses, gathering 8,400 human ratings with detailed explanations. The study finds that current LLMs consistently exhibit the “Law of the Weakest Link,” where cross-capability performance is significantly constrained by the weakest component. This highlights the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can do lots of things. But sometimes they’re not as good at doing multiple things together as they are at doing one thing alone. This paper looks at what happens when we try to use these models to do multiple tasks at once, and how they might get better at it in the future. |
Keywords
» Artificial intelligence » Large language model
