NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning

1999-01-07 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

NTS-CoT is a novel framework designed to mitigate hallucinations in Large Language Model (LLM)-based news timeline summarization (TLS). It addresses two primary hallucination types: unfaithful content during news summarization and information omission in date-event summarization. The framework integrates three key modules: Element-CoT, which captures essential news elements for faithful single-document summarization; a Date Selection module that combines temporal saliency and event prominence for timestamp identification; and Causal-CoT, which infers causal relationships across multiple documents to reduce omissions in date-event summaries. Quantitative experiments on three TLS benchmarks, including Timeline17, Crisis, and Entities, demonstrate NTS-CoT's superior performance, achieving improvements of up to 23.4% in AR-1, 33.4% in AR-2, and 10.0% in Date-F1 compared to state-of-the-art baselines like LLM-TLS. Human evaluations further confirmed its effectiveness, with 67.74% preference for faithfulness and 54.38% for completeness.

Key takeaway

For Machine Learning Engineers developing LLM-based news timeline summarization systems, you should integrate structured Chain-of-Thought (CoT) reasoning. Implement Element-CoT to ground single-document summaries in verifiable news elements. Also, use Causal-CoT for multi-document summarization. This reduces information omission by inferring causal relationships. This approach significantly improves summary faithfulness and completeness, making your systems more reliable for critical applications.

Key insights

Chain-of-Thought reasoning effectively mitigates hallucinations in LLM-based news timeline summarization by structuring information extraction and causal inference.

Principles

• Hallucinations in TLS manifest as unfaithful content or information omission.
• CoT can guide LLMs to focus on key elements for faithful summarization.
• Causal reasoning across documents reduces information omission.

Method

NTS-CoT uses Element-CoT for single-document element extraction and summarization, a date selection module balancing temporal saliency and event prominence, and Causal-CoT for multi-document causal reasoning and post-editing.

In practice

• Use Element-CoT to extract 5W1H elements for faithful news summaries.
• Apply Causal-CoT to infer causal links across event clusters.
• Balance temporal saliency and event prominence for date ranking.

Topics

• News Timeline Summarization
• LLM Hallucination Mitigation
• Chain-of-Thought Reasoning
• Element-CoT
• Causal-CoT
• Multi-document Summarization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning — NTS-CoT uses Chain-of-Thought reasoning to reduce hallucinations and omissions in LLM-based news timeline summarization.
• Type V Hallucination: When LLMs Go Off the Rails — Type V detection uses Geometric Algebra to assess LLM reasoning coherence, catching process failures missed by claim-based checks.
• Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks — CoT prompting significantly improves LLM performance in complex code deobfuscation tasks.
• Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs — Chain-of-Thought prompting degrades visual spatial reasoning in multimodal LLMs due to shortcut learning and hallucination.
• Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning — NLDD quantifies CoT faithfulness, revealing models can achieve high accuracy without causally relying on their generated reasoning.
• Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version) — Integrating lightweight knowledge graphs into RAG systems significantly reduces LLM hallucination and improves complex QA accuracy.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.