Scholarlink

As Large Language Models (LLMs) become increasingly ubiquitous in data-driven methods for multiple information processing tasks, so is also more significant the need to provide explainability mechanisms for these methods. In this work, we tackle a paradigmatic instance of the family of Question Answering problems by the means of a general approach based on Retrieval-augmented Generation (RAG). We focus not only on the performance for different parameter configurations but, in particular, on augmentation strategies that inquire the very generator LLM about its own interpretations behind the answer that it provides for a question.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Explaining LLM-Based Question Answering via the Self-interpretations of a Model

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Darío Garigliotti

摘要

As Large Language Models (LLMs) become increasingly ubiquitous in data-driven methods for multiple information processing tasks, so is also more significant the need to provide explainability mechanisms for these methods. In this work, we tackle a paradigmatic instance of the family of Question Answering problems by the means of a general approach based on Retrieval-augmented Generation (RAG). We focus not only on the performance for different parameter configurations but, in particular, on augmentation strategies that inquire the very generator LLM about its own interpretations behind the answer that it provides for a question.