Summary of Dynamic Evaluation Of Large Language Models by Meta Probing Agents, By Kaijie Zhu et al.
Dynamic Evaluation of Large Language Models by Meta Probing Agents
by Kaijie Zhu, Jindong Wang, Qinlin Zhao, Ruochen Xu, Xing Xie
First submitted to arxiv on: 21 Feb 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 This paper proposes Meta Probing Agents (MPA), a dynamic evaluation protocol inspired by psychometrics to evaluate Large Language Models (LLMs). MPA transforms an original evaluation problem into a new one, focusing on three basic cognitive abilities: language understanding, problem-solving, and domain knowledge. This protocol extends the previous DyVal framework, providing a multifaceted analysis of LLMs’ abilities. The authors conducted extensive evaluations using MPA and found that most LLMs achieve poorer performance, highlighting room for improvement. Additionally, they demonstrated a strong correlation between the basic abilities and an implicit Matthew effect on model size, where larger models possess stronger correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well large language models work by creating new tests to see what they’re good at. These tests are based on three important skills: understanding language, solving problems, and knowing about a specific area of knowledge. The researchers used this approach to evaluate many different language models and found that most of them didn’t do as well as we thought. They also discovered that bigger models tend to be better at certain tasks, but not always. |
Keywords
* Artificial intelligence * Language understanding
