The paper "Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification" by Anisha Gunjal and Greg Durrett from the University of Texas at Austin addresses the challenge of fact-checking large language model (LLM) generations to combat hallucinations. The authors propose a new concept called "molecular facts," which balances the granularity of atomic facts with their context. They define two criteria for molecular facts: decontextuality, ensuring that the facts can stand alone, and minimality, ensuring that they add minimal extra information. The paper includes a controlled experiment to assess the impact of decontextualization on error localization and evaluates various decontextualization methods. The results show that molecular facts improve both accuracy and minimality, particularly in ambiguous settings. The authors also discuss the limitations of their approach, including the scope of their evaluation and the need for further research on smaller models and other domains.The paper "Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification" by Anisha Gunjal and Greg Durrett from the University of Texas at Austin addresses the challenge of fact-checking large language model (LLM) generations to combat hallucinations. The authors propose a new concept called "molecular facts," which balances the granularity of atomic facts with their context. They define two criteria for molecular facts: decontextuality, ensuring that the facts can stand alone, and minimality, ensuring that they add minimal extra information. The paper includes a controlled experiment to assess the impact of decontextualization on error localization and evaluates various decontextualization methods. The results show that molecular facts improve both accuracy and minimality, particularly in ambiguous settings. The authors also discuss the limitations of their approach, including the scope of their evaluation and the need for further research on smaller models and other domains.