OSU-NLP-Group/llm-planning-eval

[ACL'24] Code and data of paper "When is Tree Search Useful for LLM Planning? It Depends on the Discriminator"

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Experimental

This project offers a framework and data to evaluate how well different large language model (LLM) planning strategies perform, specifically for tasks like converting natural language questions into database queries (text-to-SQL) or solving math word problems. It takes in structured datasets of natural language tasks and outputs performance metrics, helping researchers and developers understand when tree search methods enhance LLM performance. Data scientists or AI researchers working with LLMs for complex reasoning tasks would use this.

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Use this if you are a researcher or developer who needs to rigorously test and compare various LLM planning techniques for tasks involving database interaction or mathematical reasoning.

Not ideal if you are looking for a ready-to-use LLM application or a tool for general natural language processing tasks outside of planning evaluation.

LLM evaluation text-to-SQL semantic parsing AI research natural language understanding
No License Stale 6m No Package No Dependents
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Adoption 8 / 25
Maturity 8 / 25
Community 7 / 25

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Python

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Last pushed

Feb 23, 2024

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