4 research outputs found
Estimation of SARS-CoV-2 infection fatality rate by real-time antibody screening of blood donors
Retrospective Study of Blood Transfusion Complications in the Capital Region of Denmark from 1999-2017:Characteristics of Potentially “Dangerous” Blood Donors?
ThoughtSource: A central hub for large language model reasoning data
Abstract Large language models (LLMs) such as GPT-4 have recently demonstrated impressive results across a wide range of tasks. LLMs are still limited, however, in that they frequently fail at complex reasoning, their reasoning processes are opaque, they are prone to ‘hallucinate’ facts, and there are concerns about their underlying biases. Letting models verbalize reasoning steps as natural language, a technique known as chain-of-thought prompting, has recently been proposed as a way to address some of these issues. Here we present ThoughtSource, a meta-dataset and software library for chain-of-thought (CoT) reasoning. The goal of ThoughtSource is to improve future artificial intelligence systems by facilitating qualitative understanding of CoTs, enabling empirical evaluations, and providing training data. This first release of ThoughtSource integrates seven scientific/medical, three general-domain and five math word question answering datasets