2 citations · 2 across the 5 of their papers we have counts for
6 papers
PIVOT: Perplexity-Informed KD-to-RL Transition Scheduling for Vertical-Domain Few-Shot Distillation
Heng Li, Yong Zhang, Ning Cheng +5
Vertical-domain few-shot classification remains challenging for small language models, as limited supervision makes it difficult to acquire domain-specific decision knowledge. On-P…
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models
Kainan Liu, Yong Zhang, Ning Cheng +4
Parameter-Efficient Fine-Tuning (PEFT) methods, especially LoRA, are widely used for adapting pre-trained models to downstream tasks due to their computational and storage efficien…
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
Zhuochun Li, Yong Zhang, Ming Li +8
Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design. In th…
SSPO: Subsentence-level Policy Optimization
Kun Yang, Zikang chen, Yanmeng Wang +4
As a key component of large language model (LLM) post-training, Reinforcement Learning from Verifiable Rewards (RLVR) has substantially improved reasoning performance. However, exi…
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
Yong Zhang, Heng Li, Yanwen Huang +6
Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Existing context compression methods typically rely on heuristic relevance estimation or…
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents
Zhigen Li, Jianxiang Peng, Yanmeng Wang +13
Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of…