Summary of Aime: Ai System Optimization Via Multiple Llm Evaluators, by Bhrij Patel et al.
AIME: AI System Optimization via Multiple LLM Evaluators
by Bhrij Patel, Souradip Chakraborty, Wesley A. Suttle, Mengdi Wang, Amrit Singh Bedi, Dinesh Manocha
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 challenges the conventional wisdom in text-based AI system optimization, where a single Large Language Model (LLM) generates an evaluation in natural language of the current output. The authors demonstrate that for complex tasks like code generation with multiple criteria to evaluate, relying on a single LLM evaluator can lead to incorrect evaluations and suboptimal performance. They propose AI system optimization via Multiple LLM Evaluators (AIME), which combines the evaluations from multiple LLMs using concatenation. The authors provide an extensive empirical study showing AIME outperforms baseline methods in code generation tasks, with improved error detection rates and success rates on LeetCodeHard and HumanEval datasets. They also highlight the importance of selecting the number of evaluators and criteria to utilize. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that using just one big language model to improve AI systems isn’t always the best way. When we try to generate code, for example, it’s important to consider multiple things at once. The authors found that when they used only one language model, mistakes in generated code often went undetected. To fix this, they created a new method called AIME, which uses multiple language models to evaluate different aspects of the AI system’s performance. This approach worked better than using just one model, with higher accuracy and fewer errors on certain datasets. |
Keywords
» Artificial intelligence » Language model » Large language model » Optimization
