1 citations · 1 across the 2 of their papers we have counts for
6 papers
Rethinking Personalization in Large Language Models at the Token Level
Chenheng Zhang, Yijun Lu, Lizhe Fang +7
With large language models (LLMs) now performing strongly across diverse tasks, there is growing demand for them to personalize outputs for individual users. Personalization is typ…
Autoregressive Models Rival Diffusion Models at ANY-ORDER Generation
Tianqi Du, Lizhe Fang, Weijie Yang +4
Diffusion language models enable any-order generation and bidirectional conditioning, offering appealing flexibility for tasks such as infilling, rewriting, and self-correction. Ho…
Language Ranker: A Lightweight Ranking framework for LLM Decoding
Chenheng Zhang, Tianqi Du, Jizhe Zhang +4
Conventional research on large language models (LLMs) has primarily focused on refining output distributions, while paying less attention to the decoding process that transforms th…
Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads
Zhoutong Wu, Yuan Zhang, Yiming Dong +4
Transformer models have driven breakthroughs across various language tasks by their strong capability to learn rich contextual representations. Scaling them to improve representati…
Advancing LLM Safe Alignment with Safety Representation Ranking
Tianqi Du, Zeming Wei, Quan Chen +2
The rapid advancement of large language models (LLMs) has demonstrated milestone success in a variety of tasks, yet their potential for generating harmful content has raised signif…
What is Wrong with Perplexity for Long-context Language Modeling?
Lizhe Fang, Yifei Wang, Zhaoyang Liu +5
Handling long-context inputs is crucial for large language models (LLMs) in tasks such as extended conversations, document summarization, and many-shot in-context learning. While r…