6 papers
RankSteer: Activation Steering for Pointwise LLM Ranking
Yumeng Wang, Catherine Chen, Suzan Verberne
Large language models (LLMs) have recently shown strong performance as zero-shot rankers, yet their effectiveness is highly sensitive to prompt formulation, particularly role-play…
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…
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…