Large language models (LLMs) can respond to free-text queries without being specifically
trained in the task in question, causing excitement and concern about their use in healthcare …

Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to
process and generate text. LLMs attracted substantial public attention after OpenAI's …

Large language models (LLMs) such as ChatGPT have seen widespread adoption due to
their ability to follow user instructions well. Developing these LLMs involves a complex yet …

Large language models (LLMs) have notably accelerated progress towards artificial general
intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing …

Abstract We introduce Inference-Time Intervention (ITI), a technique designed to enhance
the" truthfulness" of large language models (LLMs). ITI operates by shifting model activations …

Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Large language models (LLMs) have shown impressive performance on complex reasoning
by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains …

We aim to produce a smaller language model that is aligned to user intent. Previous
research has shown that applying distilled supervised fine-tuning (dSFT) on larger models …

Fine-tuning on instruction data has been widely validated as an effective practice for
implementing chat language models like ChatGPT. Scaling the diversity and quality of such …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …