1 citations · 2 across the 4 of their papers we have counts for
10 papers
Alignment Makes Language Models Normative, Not Descriptive
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
Post-training alignment optimizes language models to match human preference signals, but this objective is not equivalent to modeling observed human behavior. We compare 120 base-a…
Sequential LLM Release Facilitates Manipulation in Regulated Markets
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms. Such markets extend software-mediated commerce, but add a governance problem: independen…
Cooperation Under Network-Constrained Communication
Tommy Mordo, Omer Madmon, Moshe Tennenholtz
In this paper, we study cooperation in distributed games under network-constrained communication. Building on the framework of Monderer and Tennenholtz (1999), we derive a sufficie…
Data Sharing with a Generative AI Competitor
Boaz Taitler, Omer Madmon, Moshe Tennenholtz +1
As GenAI platforms grow, their dependence on content from competing providers, combined with access to alternative data sources, creates new challenges for data-sharing decisions.…
Fairness under Competition
Ronen Gradwohl, Eilam Shapira, Moshe Tennenholtz
Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal…
White Hat Search Engine Optimization using Large Language Models
Niv Bardas, Tommy Mordo, Oren Kurland +2
We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.