Summary of Watson: a Cognitive Observability Framework For the Reasoning Of Llm-powered Agents, by Benjamin Rombaut et al.
Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents
by Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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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 introduces Watson, a framework that provides reasoning observability into the implicit reasoning processes of agents driven by fast-thinking Large Language Models (LLMs). LLM-powered agents operate autonomously with opaque implicit reasoning, making it difficult to debug their unexpected behaviors or errors. The authors demonstrate the accuracy of the recovered implicit reasoning trace by Watson and its usefulness through debugging and improving the performance of LLM-powered agents in two scenarios: Massive Multitask Language Understanding (MMLU) benchmark and SWE-bench-lite. Using Watson, they were able to observe and identify the implicit reasoning errors and automatically provide targeted corrections at runtime that improve the Pass@1 of agents on MMLU and SWE-bench-lite by 7.58 (13.45% relative improvement) and 7.76 (12.31% relative improvement) percentage points, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer systems that use artificial intelligence more understandable. Right now, these systems are hard to debug because their thinking processes are hidden. The authors created a new tool called Watson that can see into the thought process of these systems and help fix mistakes when they happen. They tested Watson on two different tasks and showed that it can improve the performance of these systems by 7-8 percentage points. |
Keywords
» Artificial intelligence » Language understanding
