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 …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

We introduce phi-1, a new large language model for code, with significantly smaller size
than competing models: phi-1 is a Transformer-based model with 1.3 B parameters, trained …

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

Large language models (LLMs), such as GPT-4, have shown remarkable performance in
natural language processing (NLP) tasks, including challenging mathematical reasoning …

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …

Large language models (LLMs) have shown strong performance in tasks across domains
but struggle with chemistry-related problems. These models also lack access to external …

Large Multimodal Models (LMM) are built across modalities and the misalignment between
two modalities can result in" hallucination", generating textual outputs that are not grounded …

Large Language Models (LLMs) have drawn a lot of attention due to their strong
performance on a wide range of natural language tasks, since the release of ChatGPT in …

This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …