Investigating the Role of Prompting and External Tools in Hallucination Rates of LLMs

🔎 Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models

This paper examines the effectiveness of different prompting techniques and frameworks for mitigating hallucinations in large language models (LLMs). The authors investigate how these techniques, including Chain-of-Thought, Self-Consistency, and Multiagent Debate, can improve reasoning capabilities and reduce factual inconsistencies. They also explore the impact of LLM agents, which are AI systems designed to perform complex tasks by combining LLMs with external tools, on hallucination rates. The study finds that the best strategy for reducing hallucinations depends on the specific NLP task, and that while external tools can extend the capabilities of LLMs, they can also introduce new hallucinations.

📎 Link to paper