Summary of Compositional Hardness Of Code in Large Language Models — a Probabilistic Perspective, by Yotam Wolf et al.
Compositional Hardness of Code in Large Language Models – A Probabilistic Perspective
by Yotam Wolf, Binyamin Rothberg, Dorin Shteyman, Amnon Shashua
First submitted to arxiv on: 26 Sep 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 A novel limitation in large language models’ (LLMs) ability to perform complex analytical tasks is identified, highlighting an advantage for distributed problem-solving in multi-agent systems of LLMs. Specifically, sampling a single solution within the model’s context window can be ineffective for solving tasks that require multiple sub-tasks. This “in-context hardness of composition” is quantified by a generation complexity metric, showing that distributing problems among multiple agents leads to exponentially increasing efficiency gains with solution length. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools used for complex analytical tasks like code generation. Researchers have found that breaking down these tasks into smaller parts and solving them separately within the model’s “thought process” can be helpful. However, a new study reveals that LLMs struggle with performing multiple sub-tasks within the same thought process, making it harder to find correct solutions. |
Keywords
» Artificial intelligence » Context window
