ThoughtSource: A central hub for large language model reasoning data
This work addresses the problem of opaque and unreliable reasoning in AI systems for researchers and developers, but it is incremental as it builds on existing chain-of-thought prompting methods.
The authors tackled the limitations of large language models in complex reasoning by introducing ThoughtSource, a meta-dataset and software library for chain-of-thought reasoning, which integrates 15 datasets to facilitate understanding, evaluation, and training.
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.