Summary of From Feature Importance to Natural Language Explanations Using Llms with Rag, by Sule Tekkesinoglu and Lars Kunze
From Feature Importance to Natural Language Explanations Using LLMs with RAG
by Sule Tekkesinoglu, Lars Kunze
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); 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 This paper focuses on enhancing the understanding of machine learning models’ decision-making processes through conversational means. Foundation models are explored as post-hoc explainers, allowing for the elucidation of predictive models’ mechanisms. The authors introduce traceable question-answering using an external knowledge repository to inform Large Language Models (LLMs) responses within a scene understanding task. This repository contains high-level features, feature importance, and alternative probabilities. Subtractive counterfactual reasoning is employed to compute feature importance by analyzing output variations resulting from decomposing semantic features. Additionally, the authors integrate social, causal, selective, and contrastive characteristics from social science research on human explanations into a single-shot prompt for response generation. Evaluation demonstrates that LLM-generated explanations included these elements, indicating potential for bridging complex model outputs and natural language expressions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models better at explaining their decisions to humans. Right now, it’s hard for people to understand why a model made a certain choice, so this research focuses on improving that. The authors are using special kinds of computer models called Large Language Models (LLMs) to generate explanations for complex tasks like scene understanding. They’re also creating a new way of analyzing the output from these models to figure out what’s most important. By combining insights from social sciences and language processing, this research aims to make it easier for humans to understand the decisions made by these powerful models. |
Keywords
* Artificial intelligence * Machine learning * Prompt * Question answering * Scene understanding
