Summary of Llmcheckup: Conversational Examination Of Large Language Models Via Interpretability Tools and Self-explanations, by Qianli Wang et al.
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations
by Qianli Wang, Tatiana Anikina, Nils Feldhus, Josef van Genabith, Leonhard Hennig, Sebastian Möller
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers present LLMCheckup, an innovative tool that enables users to have a conversation with large language models (LLMs) about their behavior. This tool allows LLMs to generate explanations and recognize user intent without requiring fine-tuning. The authors connect the LLMs to various Explainable AI (XAI) methods, including feature attributions and self-explanations. The resulting dialogue-based explanations support follow-up questions and suggest answers. To facilitate adoption, the tool comes with tutorials for users of varying expertise levels and supports multiple input modalities. Additionally, the paper introduces a new parsing strategy that improves user intent recognition accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a special chat tool called LLMCheckup. It lets people talk to big computer language models (LLMs) about what they’re doing. The LLMs can explain themselves and figure out what you want them to do without needing extra training. This tool connects the LLMs to different ways of making things clear, like showing which features are important or letting the model explain its own thinking. The result is a conversation that answers questions and suggests next steps. To help people use it, there’s a guide for users with different levels of experience, and it works with different types of input. |
Keywords
* Artificial intelligence * Fine tuning * Parsing
