Existing open-source multimodal large language models (MLLMs) generally follow a
training process involving pre-training and supervised fine-tuning. However, these models …

Large Language Models (LLMs) are often aligned using contrastive alignment objectives
and preference pair datasets. The interaction between model, paired data, and objective …

With the advent of the big data and large language model era, zero-shot personalized rapid
customization has emerged as a significant trend. In this report, we introduce Takin …

Abstract Large Language Models have expanded the potential for clinical Natural Language
Generation (NLG), presenting new opportunities to manage the vast amounts of medical …

Large language models (LLMs) are still struggling in aligning with human preference in
complex tasks and scenarios. They are prone to overfit into the unexpected patterns or …

Large language models (LLMs) are still struggling in aligning with human preference in
complex tasks and scenarios. They are prone to overfit into the unexpected patterns or …

Direct Preference Optimization (DPO) has emerged as a prominent algorithm for the direct
and robust alignment of Large Language Models (LLMs) with human preferences, offering a …

Large language models (LLMs) have made significant advancements, but their increasing
capabilities present serious risks of misuse, particularly in open-weight models where direct …

We propose a theoretical framework demonstrating that popular Large Language Model
(LLM) alignment methods, including Reinforcement Learning from Human Feedback (RLHF) …

Emerging technologies like Artificial Intelligence and Large Language Models are often
developed in Western contexts and carry implicit values, from developer choices or …