The field of large language models (LLMs) is shifting towards a greater emphasis on hallucination detection and mitigation. Researchers are working to develop more robust metrics to understand and quantify hallucinations, as well as strategies to reduce their occurrence. Studies have shown that LLMs are prone to generating hallucinations, which can have significant consequences in applications such as clinical summarization and code generation. Some notable papers have proposed innovative approaches to addressing this challenge, including the use of mode-seeking decoding methods and NLI models for hallucination detection. Overall, the field is moving towards a more nuanced understanding of hallucinations and the development of more effective methods for mitigating their impact. Noteworthy papers include Evaluating Evaluation Metrics, which highlights the need for more robust metrics, and Triggering Hallucinations in LLMs, which proposes a prompt-based framework for systematically triggering and quantifying hallucination.
Hallucination Mitigation in Large Language Models
Sources
Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection
Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?
Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges
Information Gravity: A Field-Theoretic Model for Token Selection in Large Language Models
Jekyll-and-Hyde Tipping Point in an AI's Behavior
Waking Up an AI: A Quantitative Framework for Prompt-Induced Phase Transition in Large Language Models
Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models