Summary of Don’t Trust: Verify — Grounding Llm Quantitative Reasoning with Autoformalization, by Jin Peng Zhou et al.
Don’t Trust: Verify – Grounding LLM Quantitative Reasoning with Autoformalization
by Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q. Weinberger, Yuhuai Wu
First submitted to arxiv on: 26 Mar 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 explores the potential of large language models (LLMs) in solving mathematical quantitative reasoning problems. While LLMs like Google’s Minerva and OpenAI’s GPT families have shown impressive capabilities, they still struggle with unjustified logical and computational errors. To address this issue, researchers leverage the idea that if LLM training corpora contained formal mathematics examples, they can be prompted to translate informal mathematical statements into formal code. This approach enables automatic verification of solutions for internal consistency and rejection of inconsistent ones. The paper evaluates its method on GSM8K, MATH, and MultiArith datasets, demonstrating a consistently better heuristic than vanilla majority voting by more than 12% on GSM8K. The results improve across all datasets and LLM model sizes. The code is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers solve math problems better! Large language models are getting smarter but still make mistakes. To fix this, the researchers thought: what if we teach them how to write math problems in a special way that can be checked for errors? They tried this idea and showed it works by testing it on some big datasets. It’s like having a new tool that helps computers do math more accurately. |
Keywords
* Artificial intelligence * Gpt
