Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We present DRESS a large vision language model (LVLM) that innovatively exploits Natural
Language feedback (NLF) from Large Language Models to enhance its alignment and …

This study highlights the potential of fine-tuned ChatGPT (GPT-3.5) for automatically scoring
student written constructed responses using example assessment tasks in science …

This paper presents a comprehensive evaluation of the Optical Character Recognition
(OCR) capabilities of the recently released GPT-4V (ision), a Large Multimodal Model …

In the realm of large language models (LLMs), enhancing instruction-following capability
often involves curating expansive training data. This is achieved through two primary …

Recent work demonstrates that, after being fine-tuned on a high-quality instruction dataset,
the resulting model can obtain impressive capabilities to address a wide range of tasks …

This paper introduces an efficient strategy to transform Large Language Models (LLMs) into
Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a …

With the advancement of language models (LMs), their exposure to private data is
increasingly inevitable, and their deployment (especially for smaller ones) on personal …

Large Language Models (LLMs) have revolutionized natural language processing, yet
aligning these models with human values and preferences using RLHF remains a significant …

Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to
human instructions. The quality of instruction pairs used for tuning greatly affects the …