Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.

For example, back-door criterion (BD) (Pearl, 1995) isasound graphical condition todetermine ifthe causal effect is expressible as a covariate adjustment.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Weconsidertwoscenarios where causal effects are estimable.