Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization

Understand

Large Language Models (LLMs) have significantly advanced text generation capabilities, including tasks like summarization, often producing coherent and fluent outputs.

• However, faithfulness to source material remains a significant challenge due to the generation of hallucinations.
• While extensive research focuses on detecting and reducing these inaccuracies, less attention has been paid to the positional distribution of hallucination within generated text, particularly in long outputs.
• In this work, we investigate where hallucinations occur in LLM-based long response generation, using long document summarization as a key case study.