4 citations · 10 across the 5 of their papers we have counts for
9 papers
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model
Chenhan Yuan, Fei Huang, Ru Peng +4
Transformer-based large language models (LLMs) exhibit limitations such as generating unsafe responses, unreliable reasoning, etc. Existing inference intervention approaches attemp…
Online Merging Optimizers for Boosting Rewards and Mitigating Tax in Alignment
Keming Lu, Bowen Yu, Fei Huang +3
Effectively aligning Large Language Models (LLMs) with human-centric values while preventing the degradation of abilities acquired through Pre-training and Supervised Fine-tuning (…
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Keming Lu, Bowen Yu, Chang Zhou +1
Considerable efforts have been invested in augmenting the role-playing proficiency of open-source large language models (LLMs) by emulating proprietary counterparts. Nevertheless,…
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Keming Lu, Hongyi Yuan, Runji Lin +4
The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs…
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
Shengguang Wu, Keming Lu, Benfeng Xu +3
Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a…
Speculative Contrastive Decoding
Hongyi Yuan, Keming Lu, Fei Huang +2
Large language models~(LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-opt…