seonghyeonye/TAPP
[AAAI 2024] Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
This project helps researchers in natural language processing evaluate how well large language models (LLMs) can follow instructions. It takes in datasets of instructions and desired outputs, then applies different prompting strategies to LLMs to see how accurately they generate the correct responses. Anyone working on improving or analyzing LLMs for instruction-following tasks would find this valuable.
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Use this if you are an NLP researcher or practitioner studying the effectiveness of various prompting techniques for improving LLM's ability to follow complex instructions.
Not ideal if you are looking for a ready-to-use application for a specific real-world problem, as this is a research framework for evaluation.
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79
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2
Language
Python
License
MIT
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Last pushed
Sep 13, 2024
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