8 citations · 9 across the 3 of their papers we have counts for
9 papers
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…
Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction
Tingchen Fu, Deng Cai, Lemao Liu +2
Supervised fine-tuning (SFT) on instruction-following corpus is a crucial approach toward the alignment of large language models (LLMs). However, the performance of LLMs on standar…
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…