12 papers
Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation
Rui Li, Liyang He, Zheng Zhang +3
On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal than the outcome-level reward…
DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation
Ao Yu, Weibo Gao, Heng Zhou +6
On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questions the student already answ…
A Survey on Deep Text Hashing: Efficient Semantic Text Retrieval with Binary Representation
Liyang He, Zhenya Huang, Cheng Yang +6
With the rapid growth of textual content on the Internet, efficient large-scale semantic text retrieval has garnered increasing attention from both academia and industry. Text hash…
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Weibo Gao, Qi Liu, Rui Li +5
Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective an…
MGS3: A Multi-Granularity Self-Supervised Code Search Framework
Rui Li, Junfeng Kang, Qi Liu +5
In the pursuit of enhancing software reusability and developer productivity, code search has emerged as a key area, aimed at retrieving code snippets relevant to functionalities ba…
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation
Zheng Zhang, Ning Li, Qi Liu +6
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant document from external knowledge sources. By referencing this external knowledge,…
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models
Liyang He, Chenglong Liu, Rui Li +4
Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using annotated datasets like NLI. Yet, the reliance on manual la…
Nested Hash Layer: A Plug-and-play Module for Multiple-length Hash Code Learning
Liyang He, Yuren Zhang, Rui Li +3
Deep supervised hashing is essential for efficient storage and search in large-scale image retrieval. Traditional deep supervised hashing models generate single-length hash codes,…
PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables
Qingyang Mao, Qi Liu, Zhi Li +3
In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models (LLMs), which have revolutionized nat…
Learning Recommender Systems with Soft Target: A Decoupled Perspective
Hao Zhang, Mingyue Cheng, Qi Liu +3
Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction…
RePair: Automated Program Repair with Process-based Feedback
Yuze Zhao, Zhenya Huang, Yixiao Ma +6
The gap between the trepidation of program reliability and the expense of repairs underscores the indispensability of Automated Program Repair (APR). APR is instrumental in transfo…
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Junzhe Jiang, Shang Qu, Mingyue Cheng +8
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the…