Large Language Model
Learning Spatially-Aware Language and Audio Embeddings
Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
However, there is little to no understanding of the notion of medical safety in the context of LLMs, let alone how to evaluate and improve it. To address this gap, we first define the notion of medical safety in LLMs based on the Principles of Medical Ethics set forth by the American Medical Association.
ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence
GPT -4o, on this dataset and find that LLMs are susceptible to adopting incorrect retrieved content, overriding their own correct prior knowledge over 60% of the time. However, the more unrealistic the retrieved content is (i.e. more deviated from
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Recently, large language models (LLMs) ( e.g., GPT -3 and ChatGPT) have shown remarkable zero-shot and few-shot performance on various tasks [
Appendix overview This section provides an overview of the supplementary materials for our submission
We further focus on measuring the overlap between datasets. We check for the overlap on the level of systematic reviews based on the review's Cochrane ID.
