94 citations · 200 across the 17 of their papers we have counts for
6 papers · 1 filter
Silkie: Preference Distillation for Large Visual Language Models
Lei Li, Zhihui Xie, Mukai Li +6
This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual contex…
DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models
Shansan Gong, Mukai Li, Jiangtao Feng +2
Diffusion models have gained prominence in generating high-quality sequences of text. Nevertheless, current approaches predominantly represent discrete text within a continuous dif…
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Chenxin An, Shansan Gong, Ming Zhong +5
Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations wi…
MIT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
Lei Li, Yuwei Yin, Shicheng Li +9
Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress i…
LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark
Zhenfei Yin, Jiong Wang, Jianjian Cao +9
Large language models have emerged as a promising approach towards achieving general-purpose AI agents. The thriving open-source LLM community has greatly accelerated the developme…
In-Context Learning with Many Demonstration Examples
Mukai Li, Shansan Gong, Jiangtao Feng +4
Large pre-training language models (PLMs) have shown promising in-context learning abilities. However, due to the backbone transformer architecture, existing PLMs are bottlenecked…