Reducing Hallucinations in LLM-based Chatbots with Citation-Enhanced Generation

Core Concepts

The author proposes a post-hoc Citation-Enhanced Generation (CEG) approach to address hallucinations in LLM-based chatbots by incorporating retrieval augmentation and natural language inference technologies.

Abstract

The content discusses the challenges of hallucinations in LLM-based chatbots and introduces a novel post-hoc approach, CEG, to mitigate this issue. By combining retrieval augmentation and NLI, the framework shows promising results in detecting and regenerating responses with reduced hallucinations. Large language models (LLMs) exhibit powerful general intelligence but face challenges like producing hallucinated content. The proposed CEG framework addresses this issue post-hoc by incorporating retrieval augmentation and NLI technologies. Experiments show improved performance in detecting and regenerating responses with reduced hallucinations. Various efforts have been made to alleviate hallucination in LLM-based chatbots, such as retrieval augmented generation and reinforcement learning. The CEG framework stands out for its post-hoc approach, flexibility across different LLMs, and state-of-the-art performance on three benchmarks related to hallucination detection and response regeneration.

Stats

Our method achieves 69.45% accuracy on HaluEval dataset. CEG outperforms all baseline methods in Balanced_ACC on WikiBio GPT-3 dataset. The precision of True-9B model is 84% on WikiRetr-GPT3 dataset. SimCSE BERT has better performance than other retrievers with 76.8% accuracy on WikiRetr-GPT3 dataset.

Quotes

"Our method is a training-free plug-and-play plugin that is capable of various LLMs." "Experiments show improved performance in detecting and regenerating responses with reduced hallucinations." "The proposed CEG framework addresses this issue post-hoc by incorporating retrieval augmentation and NLI technologies."

Key Insights Distilled From

Citation-Enhanced Generation for LLM-based Chatbots

by Weitao Li,Ju... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2402.16063.pdf

Deeper Inquiries

How can the CEG framework be further optimized for real-time applications?

The CEG framework can be optimized for real-time applications by implementing efficient retrieval mechanisms to quickly search for relevant documents. This could involve using more advanced retrieval models or techniques that prioritize speed without compromising accuracy. Additionally, optimizing the citation generation module to swiftly assess the relationship between generated content and retrieved documents can enhance real-time performance. Streamlining the regeneration process by fine-tuning prompt generation algorithms and response evaluation methods can also contribute to faster responses in real-time scenarios.

What are the ethical implications of using citation-enhanced generation in AI systems?

Using citation-enhanced generation in AI systems raises several ethical considerations. One key concern is ensuring transparency and accountability in attributing information sources correctly. Properly citing references is crucial to avoid plagiarism and give credit where it's due. Moreover, there may be issues related to bias if citations are selectively chosen or manipulated to support a particular narrative. It's essential to maintain integrity and honesty when incorporating citations into generated content, as misleading or inaccurate references could have far-reaching consequences on users' trust and decision-making processes.

How might the concept of post-hoc verification be applied to other areas of natural language processing?

The concept of post-hoc verification can be applied across various areas of natural language processing (NLP) to enhance model reliability and output quality. In sentiment analysis, post-hoc verification could involve checking sentiment predictions against external data sources or user feedback after generating responses. For machine translation, verifying translated text accuracy through back-translation or reference comparison post-generation could improve translation quality. In summarization tasks, validating summary coherence with original texts through NLI-based assessments after summarizing content would ensure accurate information retention.