2 citations · 4 across the 11 of their papers we have counts for
12 papers
Learning Dynamics of Strategic Publishers in Generative AI Ecosystems
Sagie Dekel, Omer Madmon, Moshe Tennenholtz +1
Generative AI (GenAI) search systems are transforming how users access information. Unlike ranking-based search systems, where users observe a ranked list of documents, GenAI searc…
Stability in Competitive Search with Results Diversification
Itamar Reinman, Omer Madmon, Moshe Tennenholtz +1
In a competitive search setting, publishers strategically modify their documents in response to induced rankings so as to improve their future ranking. We present a novel game-theo…
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In…
Addressing Corpus Knowledge Poisoning Attacks on RAG Using Sparse Attention
Sagie Dekel, Moshe Tennenholtz, Oren Kurland
Retrieval Augmented Generation (RAG) is a highly effective paradigm for keeping LLM-based responses up-to-date and reducing the likelihood of hallucinations. Yet, RAG was recently…
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…