6 citations · 11 across the 13 of their papers we have counts for
17 papers
UserArena: Benchmarking Interactive User Simulation for Long-Horizon Shopping Agents
Jujia Zhao, Jiakai Tang, Jing Yu +6
Agentic recommendation pursues shopper goals through sequences of context-dependent decisions. Developing and evaluating such policies requires faithful recommendation world models…
Breaking Bureaucracy: Evaluating open-source LLMs for legal document review
Farrukh Baratov, Niki van Stein, Suzan Verberne
In this paper, we evaluate open-source generative LLMs on legal Natural Language Inference (NLI). Legal inspectorial processes take place in specific domains and often deal with co…
Walking the Embedding Space: Datastore Extraction from Multimodal RAG
Maria Carmen Jica, Ali Satvaty, Suzan Verberne +1
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models…
RankSteer: Can Pointwise LLM Rankers Be Calibrated at the Representation Level?
Yumeng Wang, Catherine Chen, Suzan Verberne
Large language models (LLMs) are strong zero-shot pointwise rankers, but lag behind pairwise and listwise methods. Beyond missing comparative signals, we identify a \textit{calibra…
ReportLogic: Evaluating Logical Quality in Deep Research Reports
Jujia Zhao, Zhaoxin Huan, Zihan Wang +4
Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports that support understanding and action. I…
LANCER: LLM Reranking for Nugget Coverage
Jia-Huei Ju, François G. Landry, Eugene Yang +2
Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG requires retrieval to provide documents covering a wide range of relevant…
Differentiable Semantic ID for Generative Recommendation
Junchen Fu, Xuri Ge, Alexandros Karatzoglou +4
Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most existing methods treat SIDs as…
Unifying Search and Recommendation in LLMs via Gradient Multi-Subspace Tuning
Jujia Zhao, Zihan Wang, Shuaiqun Pan +2
Search and recommendation (S&R) are core to online platforms, addressing explicit intent through queries and modeling implicit intent from behaviors, respectively. Their complement…
OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning
Ziyou Hu, Zhengliang Shi, Minghang Zhu +5
Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both training and inference. However, exist…
How role-play shapes relevance judgment in zero-shot LLM rankers
Yumeng Wang, Jirui Qi, Catherine Chen +2
Large Language Models (LLMs) have emerged as promising zero-shot rankers, but their performance is highly sensitive to prompt formulation. In particular, role-play prompts, where t…
Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
Zhengliang Shi, Lingyong Yan, Dawei Yin +3
Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques. However, effectively enabling LLMs to seek accurate knowledge…
Unifying Search and Recommendation with Dual-View Representation Learning in a Generative Paradigm
Jujia Zhao, Wenjie Wang, Chen Xu +3
Recommender systems and search engines serve as foundational elements of online platforms, with the former delivering information proactively and the latter enabling users to seek…
QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling
Yumeng Wang, Xiuying Chen, Suzan Verberne
In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This lead…
What are the limits of cross-lingual dense passage retrieval for low-resource languages?
Jie Wu, Zhaochun Ren, Suzan Verberne
In this paper, we analyze the capabilities of the multi-lingual Dense Passage Retriever (mDPR) for extremely low-resource languages. In the Cross-lingual Open-Retrieval Answer Gene…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi, Shen Gao, Xiuyi Chen +7
Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively sele…
A Multi-Agent Conversational Recommender System
Jiabao Fang, Shen Gao, Pengjie Ren +3
Due to strong capabilities in conducting fluent, multi-turn conversations with users, Large Language Models (LLMs) have the potential to further improve the performance of Conversa…