8 citations · 22 across the 16 of their papers we have counts for
19 papers
Distance-KV: Exploiting Relative Distance for Efficient Long-Context Inference
Xianpeng Shang, Canbin Huang, Jiang Li +4
The memory usage and decoding latency of LLM inference grow rapidly with context length. To reduce these costs, key-value (KV) cache compression methods selectively retain cached s…
Lookahead Routing for Large Language Models
Canbin Huang, Tianyuan Shi, Yuhua Zhu +2
Large language model (LLM) routers improve the efficiency of multi-model systems by directing each query to the most appropriate model while leveraging the diverse strengths of het…
ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
Tianjian Liu, Fanqi Wan, Jiajian Guo +1
Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs). Existing works predominantly focus on domain-specific or ta…
Mutual-Taught for Co-adapting Policy and Reward Models
Tianyuan Shi, Canbin Huang, Fanqi Wan +5
During the preference optimization of large language models (LLMs), distribution shifts may arise between newly generated model samples and the data used to train the reward model…
ThinkSwitcher: When to Think Hard, When to Think Fast
Guosheng Liang, Longguang Zhong, Ziyi Yang +1
Large reasoning models (LRMs) excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. However, this often leads to overthinking on simple tasks, resulti…
FuseRL: Dense Preference Optimization for Heterogeneous Model Fusion
Longguang Zhong, Fanqi Wan, Ziyi Yang +3
Heterogeneous model fusion enhances the performance of LLMs by integrating the knowledge and capabilities of multiple structurally diverse models. However, existing approaches ofte…
FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion
Ziyi Yang, Fanqi Wan, Longguang Zhong +3
We introduce FuseChat-3.0, a suite of large language models (LLMs) developed by integrating the strengths of heterogeneous source LLMs into more compact target LLMs. Our source mod…
Advantage-Guided Distillation for Preference Alignment in Small Language Models
Shiping Gao, Fanqi Wan, Jiajian Guo +2
Alignment techniques enable Large Language Models (LLMs) to generate outputs that align with human preferences and play a crucial role in their effectiveness. However, their impact…
Weighted-Reward Preference Optimization for Implicit Model Fusion
Ziyi Yang, Fanqi Wan, Longguang Zhong +2
While fusing heterogeneous open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models, existing fusion methods face significa…
FuseChat: Knowledge Fusion of Chat Models
Fanqi Wan, Longguang Zhong, Ziyi Yang +2
While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy…
Self-Evolution Fine-Tuning for Policy Optimization
Ruijun Chen, Jiehao Liang, Shiping Gao +2
The alignment of large language models (LLMs) is crucial not only for unlocking their potential in specific tasks but also for ensuring that responses meet human expectations and a…
BlockPruner: Fine-grained Pruning for Large Language Models
Longguang Zhong, Fanqi Wan, Ruijun Chen +2
With the rapid growth in the size and complexity of large language models (LLMs), the costs associated with their training and inference have escalated significantly. Research indi…
Knowledge Fusion of Chat LLMs: A Preliminary Technical Report
Fanqi Wan, Ziyi Yang, Longguang Zhong +3
Recently, FuseLLM introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continu…
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…
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…
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
Tao Yang, Tianyuan Shi, Fanqi Wan +4
Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in person…
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Fanqi Wan, Xinting Huang, Tao Yang +3
Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks. However, existing data employed f…
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
Tianyuan Shi, Liangzhi Li, Zijian Lin +3
Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necess…
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
Weizhou Shen, Yingqi Gao, Canbin Huang +3
Developing an efficient retriever to retrieve knowledge from a large-scale knowledge base (KB) is critical for task-oriented dialogue systems to effectively handle localized and sp…