9 citations · 30 across the 14 of their papers we have counts for
8 papers · 1 filter
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Deng Cai, Huayang Li, Tingchen Fu +11
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstra…
Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning
Sen Yang, Leyang Cui, Deng Cai +3
Iterative preference learning, though yielding superior performances, requires online annotated preference labels. In this work, we study strategies to select worth-annotating resp…
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
Yafu Li, Zhilin Wang, Leyang Cui +3
AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-para…
CORM: Cache Optimization with Recent Message for Large Language Model Inference
Jincheng Dai, Zhuowei Huang, Haiyun Jiang +4
Large Language Models (LLMs), despite their remarkable performance across a wide range of tasks, necessitate substantial GPU memory and consume significant computational resources.…
Retrieval is Accurate Generation
Bowen Cao, Deng Cai, Leyang Cui +4
Standard language models generate text by selecting tokens from a fixed, finite, and standalone vocabulary. We introduce a novel method that selects context-aware phrases from a co…
Knowledge Fusion of Large Language Models
Fanqi Wan, Xinting Huang, Deng Cai +3
While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant…
Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models
Shuming Shi, Enbo Zhao, Deng Cai +3
We present Inferflow, an efficient and highly configurable inference engine for large language models (LLMs). With Inferflow, users can serve most of the common transformer models…
Knowledge Verification to Nip Hallucination in the Bud
Fanqi Wan, Xinting Huang, Leyang Cui +3
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible…