Summary of On the Brittle Foundations Of React Prompting For Agentic Large Language Models, by Mudit Verma et al.
On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models
by Mudit Verma, Siddhant Bhambri, Subbarao Kambhampati
First submitted to arxiv on: 22 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 claims surrounding ReAct-based prompting, a method used to enhance the sequential decision-making abilities of agentic Large Language Models (LLMs). While some methods have gained popularity for their supposed ability to improve LLM reasoning, it is unclear what drives this improvement. The study performs a sensitivity analysis on these claims by introducing systematic variations to input prompts and finds that performance is minimally influenced by the original claimed mechanisms. Instead, the performance of LLMs is driven by the similarity between input example tasks and queries, which increases cognitive burden on humans. This investigation shows that perceived reasoning abilities of LLMs stem from exemplar-query similarity and approximate retrieval rather than inherent reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well large language models can make decisions. Some people use a technique called ReAct-based prompting to try to improve these decision-making skills, but it’s not clear what actually makes it work better. The researchers tested different ways of using this technique and found that it doesn’t really do anything special – it just works because the examples used to train the model are similar to the problems it’s trying to solve. This means that humans have to come up with lots of specific examples, which is hard. |
Keywords
» Artificial intelligence » Prompting
