1 citations · 4 across the 12 of their papers we have counts for
13 papers
SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating
Zequn Xie, Junjie Wang, Dan Yang +4
Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost. Driven by accuracy-focused…
CTRL-RAG: Contrastive Likelihood Reward Based Reinforcement Learning for Context-Faithful RAG Models
Zhehao Tan, Yihan Jiao, Dan Yang +8
With the growing use of Retrieval-Augmented Generation (RAG), training large language models (LLMs) for context-sensitive reasoning and faithfulness is increasingly important. Exis…
WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning
Junjie Wang, Zequn Xie, Dan Yang +9
Deep Research systems based on web agents have shown strong potential in solving complex information-seeking tasks, yet their search efficiency remains underexplored. We observe th…
A Medical Multimodal Diagnostic Framework Integrating Vision-Language Models and Logic Tree Reasoning
Zelin Zang, Wenyi Gu, Siqi Ma +6
With the rapid growth of large language models (LLMs) and vision-language models (VLMs) in medicine, simply integrating clinical text and medical imaging does not guarantee reliabl…
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs
Meixiu Long, Duolin Sun, Dan Yang +12
Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitati…
HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering
Duolin Sun, Dan Yang, Yue Shen +7
The Retrieval-Augmented Generation (RAG) approach enhances question-answering systems and dialogue generation tasks by integrating information retrieval (IR) technologies with larg…
DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
Duolin Sun, Meixiu Long, Dan Yang +9
Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matc…
PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation
Zhehao Tan, Yihan Jiao, Dan Yang +7
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM's ability to generate responses based on the combination…
POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applications
Chunjing Gan, Dan Yang, Binbin Hu +5
Large language models (LLMs) have become a disruptive force in the industry, introducing unprecedented capabilities in natural language processing, logical reasoning and so on. How…
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Junjie Wang, Mingyang Chen, Binbin Hu +10
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enha…
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Chunjing Gan, Dan Yang, Binbin Hu +9
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucinati…
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
Junjie Wang, Dan Yang, Binbin Hu +3
In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this is…
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
Chunjing Gan, Dan Yang, Binbin Hu +6
Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user pr…