Abstract Large Language Models (LLMs) can achieve strong performance on many tasks by
producing step-by-step reasoning before giving a final output, often referred to as chain-of …

We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what
extent a given language model, such as GPT models, can simulate different aspects of …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Large language models have demonstrated outstanding performance on a wide range of
tasks such as question answering and code generation. On a high level, given an input, a …

We present symbol tuning-finetuning language models on in-context input-label pairs where
natural language labels (eg," positive/negative sentiment") are replaced with arbitrary …

Work on scaling laws has found that large language models (LMs) show predictable
improvements to overall loss with increased scale (model size, training data, and compute) …

Artificial intelligence (AI) tools like ChatGPT demonstrate the potential to support
personalized and adaptive learning experiences. This study explores how ChatGPT can …

State-of-the-art language models can exhibit impressive reasoning refinement capabilities
on math, science or coding tasks. However, recent work demonstrates that even the best …

Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive
uncertainty. We take a close look into this problem, aiming to answer two questions:(1) Do …