Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval
This work addresses the challenge of improving reliability in synthesis planning for chemists, though it is incremental as it builds on existing methods for knowledge graph interaction.
The authors tackled the problem of LLMs hallucinating or providing outdated suggestions in chemical synthesis planning by grounding them in reaction knowledge graphs, finding that one-shot prompting with aligned exemplars consistently performed best in retrieval accuracy.
Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions with a reaction knowledge graph by casting reaction path retrieval as a Text2Cypher (natural language to graph query) generation problem, and define single- and multi-step retrieval tasks. We compare zero-shot prompting to one-shot variants using static, random, and embedding-based exemplar selection, and assess a checklist-driven validator/corrector loop. To evaluate our framework, we consider query validity and retrieval accuracy. We find that one-shot prompting with aligned exemplars consistently performs best. Our checklist-style self-correction loop mainly improves executability in zero-shot settings and offers limited additional retrieval gains once a good exemplar is present. We provide a reproducible Text2Cypher evaluation setup to facilitate further work on KG-grounded LLMs for synthesis planning. Code is available at https://github.com/Intelligent-molecular-systems/KG-LLM-Synthesis-Retrieval.