Summary of Token-budget-aware Llm Reasoning, by Tingxu Han et al.
Token-Budget-Aware LLM Reasoning
by Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, Zhenyu Chen
First submitted to arxiv on: 24 Dec 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 addresses the crucial role of reasoning in large language models (LLMs) for excelling in various tasks. The Chain-of-Thought (CoT) reasoning method enhances LLM performance by breaking down problems into intermediate steps, but it comes at a cost due to increased token usage and expenses. To mitigate this issue, the authors propose a token-budget-aware LLM reasoning framework that dynamically estimates token budgets based on problem complexity and guides the reasoning process accordingly. Experimental results demonstrate that this approach effectively reduces token costs while only marginally impacting performance, offering a practical solution for balancing efficiency and accuracy in LLM reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models be better at solving problems by breaking them down into smaller steps. The problem is that these models use a lot of “tokens” (like words) to do this, which can get expensive. To fix this, the authors came up with a new way for the model to decide how many tokens it needs based on the difficulty of the problem. This makes the model more efficient without sacrificing too much accuracy. |
Keywords
* Artificial intelligence * Token
