11 citations · 12 across the 9 of their papers we have counts for
9 papers
When Top-K Misses the Decision: Tool-Call Drift in Multi-Teacher On-Policy Distillation
Jiabin Shen, Guang Chen, Chengjun Mao
Top- teacher logits make on-policy distillation tractable, but retained teacher mass does not certify student-relative gradient fidelity. We study routed, two-teacher tool-use d…
Answer Presence Drives RAG Rewriting Gains
Yuejie Li, Yueying Hua, Ke Yang +7
Retrieval-augmented QA pipelines often route retrieved passages through an LLM \emph{rewriter} before a smaller reader, lifting F1 by tens of points on multi-hop benchmarks; this g…
Attention-guided Evidence Grounding for Spoken Question Answering
Ke Yang, Bolin Chen, Yuejie Li +5
Spoken Question Answering (Spoken QA) presents a challenging cross-modal problem: effectively aligning acoustic queries with textual knowledge while avoiding the latency and error…
Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration
Yuejie Li, Ke Yang, Tao Wang +3
Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing method…
Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning
Yekun Zhu, Guang Chen, Chengjun Mao
Large Language Models (LLMs) with chains-of-thought have demonstrated strong performance on an increasing range of tasks, particularly those involving complex logical reasoning. Ho…
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
Wei Dai, Yingmin Su, Xiaofeng Pan
In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in. However, users' preferences for t…
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation
Ming Li, Naiyin Liu, Xiaofeng Pan +5
Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely o…
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios
Xiaofeng Pan, Ming Li, Jing Zhang +5
Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctua…
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction
Xiaofeng Pan, Yibin Shen, Jing Zhang +5
Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave diff…