Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

2026-09-04

AI Summary

研究审计了22种前沿模型在12个回归基准上直接检索已发表数值的情况，探讨了不同推理层次对检索的影响，并测试了一种中断检索的方法。

Abstract

Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, but accuracy cannot distinguish a model that predicts a property from one that retrieves a published number. We audit 22 frontier models on 12 regression benchmarks for verbatim retrieval and find that it is widespread but relatively benchmark-specific: on five datasets more than $50\%$ of the LLMs show verbatim retrieval, while on the remaining datasets it appears only in isolated cells. We run our experiments at two reasoning levels and find that reasoning changes retrieval. The same experiments, on the same molecules and with the same prompt, are flagged $89\%$ more often at the higher reasoning level than at the lowest one. Finally, we test a way to interrupt retrieval in our most contaminated cases, and find that the strongest models in some cases still recognise a combination of transformed SMILES strings and original labels. Furthermore, suppressing retrieval moves the prediction errors of the different models closer together in relative terms, while their differing use of verbatim retrieval spreads them apart. This indicates that the general predictive capability of an LLM is not determined solely by the amount of memorised values. This work provides an overview of the amount and depth of verbatim retrieval in molecular regression benchmarks using LLMs.

Problem

• large language models
• molecular property benchmarks
• verbatim retrieval
• model evaluation

Innovation

• verbatim retrieval
• molecular property benchmarks
• large language models (LLMs)
• reasoning levels