Inconsistency of LLMs in Molecular Representations

Abstract

Large language models (LLM) have demonstrated remarkable capabilities in chemistry, yet their ability to capture intrinsic chemistry remains uncertain. Within any familiar, chemically equivalent representation family, rigorous chemical reasoning should be representation-invariant, yielding consistent predictions across these representations. Here, we introduce the first systematic benchmark to evaluate the consistency of LLMs across key chemistry tasks. We curated the benchmark using paired representations of SMILES strings and IUPAC names. We find that the state-of-the-art general LLMs exhibit strikingly low consistency rates (≤1%). Even after finetuning on our dataset, models still generate inconsistent predictions. To address this, we incorporate a sequence-level symmetric Kullback–Leibler (KL) divergence loss as a consistency regularizer. While this intervention improves surface-level consistency, it fails to enhance accuracy, suggesting that consistency and accuracy are orthogonal properties. These findings indicate that we must consider both consistency and accuracy to properly assess LLMs' capabilities in scientific reasoning.