Hallucinations Can Improve Large Language Models in Drug Discovery
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.
6793a52265c4dd63499ca548
Shuzhou Yuan
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2025-01-24T14:52:27.570Z
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Shuzhou Yuan
user
shuzyuan
6793a52265c4dd63499ca549
Michael Färber
Hallucinations Can Improve Large Language Models in Drug Discovery
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.
Hallucinations in Large Language Models can enhance their performance in drug discovery tasks by improving the quality of generated molecular descriptions and task-specific outcomes.
Large Language Models LLMs hallucinations SMILES string molecular descriptions ROC-AUC Llama-3.1-8B GPT-4o