Steering veridical large language model analyses by correcting and enriching generated database queries: first steps toward ChatGPT bioinformatics

Large language models (LLMs) leverage factual knowledge from pretraining. Yet this knowledge remains incomplete and sometimes challenging to retrieve-especially in scientific domains not extensively covered in pretraining datasets and where information is still evolving. Here, we focus on genomics and bioinformatics. We confirm and expand upon issues with plain ChatGPT functioning as a bioinformatics assistant. Poor data retrieval and hallucination lead ChatGPT to err, as do incorrect sequence manipulations. To address this, we propose a system basing LLM outputs on up-to-date, authoritative facts and facilitating LLM-guided data analysis. Specifically, we introduce NagGPT, a middleware tool to insert between LLMs and databases, designed to bridge gaps in LLM knowledge and usage of database application programming interfaces. NagGPT proxies LLM-generated database queries, with special handling of incorrect queries. It acts as a gatekeeper between query responses and the LLM prompt, redirecting large responses to files but providing a synthesized snippet and injecting comments to steer the LLM. A companion OpenAI custom GPT, Genomics Fetcher-Analyzer, connects ChatGPT with NagGPT. It steers ChatGPT to generate and run Python code, performing bioinformatics tasks on data dynamically retrieved from a dozen common genomics databases (e.g. NCBI, Ensembl, UniProt, WormBase, and FlyBase). We implement partial mitigations for encountered challenges: detrimental interactions between code generation style and data analysis, confusion between database identifiers, and hallucination of both data and actions taken. Our results identify avenues to augment ChatGPT as a bioinformatics assistant and, more broadly, to improve factual accuracy and instruction following of unmodified LLMs.