2 citations · 4 across the 12 of their papers we have counts for
6 papers · 1 filter
RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Tommy Mordo, Sagie Dekel, Omer Madmon +2
Competitive search is a setting where document publishers modify them to improve their ranking in response to a query. Recently, publishers have increasingly leveraged LLMs to gene…
On the Merits of LLM-Based Corpus Enrichment
Gal Zur, Tommy Mordo, Moshe Tennenholtz +1
Generative AI (genAI) technologies -- specifically, large language models (LLMs) -- and search have evolving relations. We argue for a novel perspective: using genAI to enrich a do…
Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrieval
Yu-An Liu, Haya Nachimovsky, Ruqing Zhang +3
With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its…
A Multi-Agent Perspective on Modern Information Retrieval
Haya Nachimovsky, Moshe Tennenholtz, Oren Kurland
The rise of large language models (LLMs) has introduced a new era in information retrieval (IR), where queries and documents that were once assumed to be generated exclusively by h…
CSP: A Simulator For Multi-Agent Ranking Competitions
Tommy Mordo, Tomer Kordonsky, Haya Nachimovsky +2
In ranking competitions, document authors compete for the highest rankings by modifying their content in response to past rankings. Previous studies focused on human participants,…
Near-Linear MIR Algorithms for Stochastically-Ordered Priors
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
With the rise of online applications, recommender systems (RSs) often encounter constraints in balancing exploration and exploitation. Such constraints arise when exploration is ca…