5 papers
Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
Serin Kim, Sangam Lee, Dongha Lee
Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail of their intent, practical web agents…
BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback
Hyunseo Kim, Sangam Lee, Kwangwook Seo +1
Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cognitive burden compared to traditi…
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths
Sangam Lee, Ryang Heo, SeongKu Kang +3
Generative retrieval directly decode a document identifier (i.e., docid) in response to a query, making it impossible to provide users with explanations as an answer for ``why is t…
SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization
Sunghwan Kim, Wooseok Jeong, Serin Kim +2
Search-Augmented Generative Engines (SAGE) have emerged as a new paradigm for information access, bridging web-scale retrieval with generative capabilities to deliver synthesized a…
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
Sangam Lee, Ryang Heo, SeongKu Kang +1
Existing dense retrieval models struggle with reasoning-intensive retrieval task as they fail to capture implicit relevance that requires reasoning beyond surface-level semantic in…