yubol-bobo/MT-Consistency

This repo investigates LLMs' tendency to exhibit acquiescence bias in sequential QA interactions. Includes evaluation methods, datasets, benchmarks, and experiment code to assess and mitigate vulnerabilities in conversational consistency and robustness, offering a reproducible framework for future research.

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Experimental

This project helps evaluate how consistently large language models (LLMs) respond during multi-turn conversations, especially in critical applications. It takes an LLM's responses to a series of related questions and assesses how stable and reliable those answers are over time. This is for researchers and developers working on AI applications where consistent, trustworthy LLM behavior is essential, such as in finance or healthcare.

No commits in the last 6 months.

Use this if you need to rigorously test the consistency and reliability of an LLM's answers across multiple follow-up questions or conversational turns.

Not ideal if you are looking for a general-purpose LLM evaluation tool for single-turn accuracy or creative writing, rather than multi-turn consistency.

LLM-evaluation AI-safety conversational-AI AI-research AI-reliability
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 3 / 25

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49

Forks

1

Language

Python

License

MIT

Last pushed

Sep 23, 2025

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