16 citations · 28 across the 9 of their papers we have counts for
10 papers
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI
Ruijia Yang, Bing Xu, Zulong Chen +5
Recruitment applications span diverse operations, from drafting job descriptions and interpreting queries to organizing profiles, assessing fit, and recommending candidates and job…
Bash-Commenter: Leveraging Syntax-Aware Preference Optimization to Reinforce Large Language Model for Bash Code Comment Generation
Lei Yu, Jingyuan Zhang, Xin Wang +5
Bash script comprehension is challenging due to Bash's syntactic freedom and complex command structures. Despite its critical role in system administration, Bash scripts often lack…
Multi-domain Multi-modal Document Classification Benchmark with a Multi-level Taxonomy
Denghao Ma, Qing Liu, Zulong Chen +5
Document classification forms the backbone of modern enterprise content management, yet existing benchmarks remain trapped in oversimplified paradigms -- single domain settings wit…
SQL-Commenter: Aligning Large Language Models for SQL Comment Generation with Direct Preference Optimization
Lei Yu, Peng Wang, Jingyuan Zhang +6
SQL query comprehension is a significant challenge due to complex syntax, diverse join types, and deep nesting. Many queries lack adequate comments, severely hindering code readabi…
Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking
Songhao Wu, Quan Tu, Mingjie Zhong +4
In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedb…
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search
Songhao Wu, Quan Tu, Hong Liu +6
Session search involves a series of interactive queries and actions to fulfill user's complex information need. Current strategies typically prioritize sequential modeling for deep…
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning
Hong Liu, Saisai Gong, Yixin Ji +3
With the rapid advancement of pre-trained large language models (LLMs), recent endeavors have leveraged the capabilities of LLMs in relevance modeling, resulting in enhanced perfor…
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search
Kaixin Wu, Yixin Ji, Zeyuan Chen +9
Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements o…
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
Zeyuan Chen, Haiyan Wu, Kaixin Wu +5
Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditio…
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
Chen Jiang, Hong Liu, Xuzheng Yu +8
In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aim…