Summary of Honest Ai: Fine-tuning “small” Language Models to Say “i Don’t Know”, and Reducing Hallucination in Rag, by Xinxi Chen et al.

Honest AI: Fine-Tuning “Small” Language Models to Say “I Don’t Know”, and Reducing Hallucination in RAG

by Xinxi Chen, Li Wang, Wei Wu, Qi Tang, Yiyao Liu

First submitted to arxiv on: 13 Oct 2024

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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 The paper addresses a crucial challenge in Large Language Models (LLMs), specifically hallucination, which is critical for enterprise applications that rely on accurate information. To combat this issue, researchers have explored two primary approaches: Retrieval-Augmented Generation (RAG) and fine-tuning LLMs with new information and desired output styles. This paper proposes Honest AI, a novel strategy to fine-tune “small” language models to explicitly say “I don’t know” when they are unsure, thereby reducing hallucination. The proposed approach ranked 1st in Task 2 for the false premise question. Alternative RAG approaches include using search engine and knowledge graph results, fine-tuning base LLMs with new information, and combining both methods. Although all approaches improve LLM performance, fine-tuning is necessary to achieve better results. The hybrid approach achieved the highest score in the CRAG benchmark. Notably, the proposed approach emphasizes the use of relatively small models with fewer than 10 billion parameters, promoting resource efficiency.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper talks about a problem with Large Language Models (LLMs) that makes them give out wrong information sometimes. This is a big deal for important applications like business uses. To fix this, researchers have been trying two main approaches: giving LLMs more information and training them to be honest when they’re unsure. The paper proposes a new way called Honest AI that helps “small” language models say “I don’t know” instead of making things up. This approach worked really well in some tests. Other ways to fix the problem include using search engines, knowledge graphs, or combining these methods. Overall, the goal is to make LLMs more reliable and efficient.