Summary of Citation-enhanced Generation For Llm-based Chatbots, by Weitao Li et al.
Citation-Enhanced Generation for LLM-based Chatbots
by Weitao Li, Junkai Li, Weizhi Ma, Yang Liu
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to addressing the issue of hallucinated content in large language model-based chatbots. The authors suggest that current efforts to alleviate this problem, such as retrieval-augmented generation and reinforcement learning with human feedback, require additional training and data annotation. Instead, they propose a post-hoc Citation-Enhanced Generation (CEG) approach that incorporates a retrieval module and natural language inference-based citation generation module. This method can regenerate responses until all statements are supported by citations, making it a plug-and-play plugin that can be used with various large language models. The authors evaluate their framework on three benchmarks and show that it outperforms state-of-the-art methods in both hallucination detection and response regeneration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make chatbots better at responding to questions without making things up. Chatbots are really good at talking, but sometimes they say things that aren’t true. The researchers who wrote this paper want to fix that problem. They came up with a new way to check if the chatbot’s answers have enough evidence to back them up. This method is special because it doesn’t need any extra training or data. It just works by looking for more information when the chatbot can’t find enough to support its answer. |
Keywords
» Artificial intelligence » Hallucination » Inference » Large language model » Reinforcement learning » Retrieval augmented generation
