12 citations · 60 across the 72 of their papers we have counts for
12 papers · 1 filter
LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
Shihan Dou, Enyu Zhou, Yan Liu +13
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.…
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
Jingcong Liang, Rong Ye, Meng Han +6
The knowledge graph is a structure to store and represent knowledge, and recent studies have discussed its capability to assist language models for various applications. Some varia…
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Wei Shen, Rui Zheng, Wenyu Zhan +5
Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values. This alignment requires a vast corpus of human…
Making Harmful Behaviors Unlearnable for Large Language Models
Xin Zhou, Yi Lu, Ruotian Ma +3
Large language models (LLMs) have shown great potential as general-purpose AI assistants in various domains. To meet the requirements of different applications, LLMs are often cust…
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation
Yikun Wang, Rui Zheng, Haoming Li +3
Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. This skill can be developed using supervised fine-tuning with extensive hum…
Unveiling A Core Linguistic Region in Large Language Models
Jun Zhao, Zhihao Zhang, Yide Ma +4
Brain localization, which describes the association between specific regions of the brain and their corresponding functions, is widely accepted in the field of cognitive science as…
Orthogonal Subspace Learning for Language Model Continual Learning
Xiao Wang, Tianze Chen, Qiming Ge +6
Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their perform…
RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms
Enyu Zhou, Rui Zheng, Zhiheng Xi +7
Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors. However, current research ten…
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
Xiao Wang, Yuansen Zhang, Tianze Chen +9
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of th…
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization
Ningyu Xu, Qi Zhang, Jingting Ye +2
Large language models (LLMs) have exhibited considerable cross-lingual generalization abilities, whereby they implicitly transfer knowledge across languages. However, the transfer…
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning
Rui Zheng, Wei Shen, Yuan Hua +9
The success of AI assistants based on language models (LLMs) hinges crucially on Reinforcement Learning from Human Feedback (RLHF), which enables the generation of responses more a…
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
Junjie Ye, Jie Zhou, Junfeng Tian +4
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance…