DFKI-NLP/LLMCheckup

Code for the NAACL 2024 HCI+NLP Workshop paper "LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-explanation" (Wang et al. 2024)

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Experimental

This tool helps researchers and AI practitioners understand why a Large Language Model (LLM) produces a particular answer. You input an LLM's response to a prompt, and it outputs explanations using various interpretability methods, revealing the reasoning behind the LLM's output. It's designed for those who need to debug, audit, or gain insight into their LLM's behavior.

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Use this if you need to analyze and explain the outputs of popular LLMs like Llama2, Mistral, or Stable Beluga 2, especially when working with text, image, or audio inputs.

Not ideal if you are looking for a pre-packaged, zero-setup solution for a specific LLM task rather than a tool for in-depth model interrogation.

AI-explainability LLM-auditing model-debugging natural-language-understanding AI-research
No License Stale 6m No Package No Dependents
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Adoption 5 / 25
Maturity 8 / 25
Community 6 / 25

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Python

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Last pushed

Mar 24, 2024

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