Large Language Models (LLMs) have demonstrated capabilities for natural language formulations of molecular property prediction tasks, but little is known about how performance depends on the representation of input molecules to the model; the status quo approach is to use SMILES strings, although alternative chemical notations convey molecular information differently, each with their own strengths and weaknesses.To learn more about molecular string representation preferences in LLMs, we compare the performance of four recent models-GPT-4o, Gemini 1.5 Pro, Llama 3.1 405b, and Mistral Large 2-on molecular property prediction tasks from the MoleculeNet benchmark across five different molecular string representations: SMILES, DeepSMILES, SELF-IES, InChI, and IUPAC names.We find statistically significant zero-and few-shot preferences for InChI and IUPAC names, potentially due to representation granularity, favorable tokenization, and prevalence in pretraining corpora.This contradicts previous assumptions that molecules should be presented to LLMs as SMILES strings.When these preferences are taken advantage of, few-shot performance rivals or surpasses many previous conventional approaches to property prediction, with the advantage of explainable predictions through chain-of-thought reasoning not held by taskspecific models.

When evaluating large language models (LLMs) with multiple-choice question answering (MCQA), it is common to end the prompt with the string "Answer:" to facilitate automated answer extraction via next-token probabilities.However, there is no consensus on how to tokenize the space following the colon, often overlooked as a trivial choice.In this paper, we uncover accuracy differences of up to 11% due to this (seemingly irrelevant) tokenization variation as well as reshuffled model rankings, raising concerns about the reliability of LLM comparisons in prior work.Surprisingly, we are able to recommend one specific strategy -tokenizing the space together with the answer letter -as we observe consistent and statistically significant performance improvements.Additionally, it improves model calibration, enhancing the reliability of the model's confidence estimates.Our findings underscore the importance of careful evaluation design and highlight the need for standardized, transparent evaluation protocols to ensure reliable and comparable results.