title: Hallucinations Can Improve Large Language Models in Drug Discovery
summary: Concerns about hallucinations in Large Language Models (LLMs) have been
  raised by researchers, yet their potential in areas where creativity is vital,
  such as drug discovery, merits exploration. In this paper, we come up with the
  hypothesis that hallucinations can improve LLMs in drug discovery. To verify
  this hypothesis, we use LLMs to describe the SMILES string of molecules in
  natural language and then incorporate these descriptions as part of the prompt
  to address specific tasks in drug discovery. Evaluated on seven LLMs and five
  classification tasks, our findings confirm the hypothesis: LLMs can achieve
  better performance with text containing hallucinations. Notably, Llama-3.1-8B
  achieves an 18.35% gain in ROC-AUC compared to the baseline without
  hallucination. Furthermore, hallucinations generated by GPT-4o provide the most
  consistent improvements across models. Additionally, we conduct empirical
  analyses and a case study to investigate key factors affecting performance and
  the underlying reasons. Our research sheds light on the potential use of
  hallucinations for LLMs and offers new perspectives for future research
  leveraging LLMs in drug discovery.
paper_authors._id: 6793a52265c4dd63499ca548
paper_authors.name: Shuzhou Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-24T14:52:27.570Z
paper_authors._id: 662ce44c8b8705f30371fba8
paper_authors.avatarUrl: /avatars/b96a25a8c124e7caa9de06b7188bdc15.svg
paper_authors.fullname: Shuzhou Yuan
paper_authors.type: user
paper_authors.user: shuzyuan
paper_authors._id: 6793a52265c4dd63499ca549
paper_authors.name: Michael Färber
paper_title: Hallucinations Can Improve Large Language Models in Drug Discovery
paper_summary: Concerns about hallucinations in Large Language Models (LLMs) have been
  raised by researchers, yet their potential in areas where creativity is vital,
  such as drug discovery, merits exploration. In this paper, we come up with the
  hypothesis that hallucinations can improve LLMs in drug discovery. To verify
  this hypothesis, we use LLMs to describe the SMILES string of molecules in
  natural language and then incorporate these descriptions as part of the prompt
  to address specific tasks in drug discovery. Evaluated on seven LLMs and five
  classification tasks, our findings confirm the hypothesis: LLMs can achieve
  better performance with text containing hallucinations. Notably, Llama-3.1-8B
  achieves an 18.35% gain in ROC-AUC compared to the baseline without
  hallucination. Furthermore, hallucinations generated by GPT-4o provide the most
  consistent improvements across models. Additionally, we conduct empirical
  analyses and a case study to investigate key factors affecting performance and
  the underlying reasons. Our research sheds light on the potential use of
  hallucinations for LLMs and offers new perspectives for future research
  leveraging LLMs in drug discovery.
paper_ai_keywords: LLMs (Large Language Models) hallucinations drug discovery SMILES string natural languages ROC-AUC prompt Feature tasks Llama-3.1-8B GPT-4o empirical analyses case study key factors affecting performances underlying reasons