12 papers
Who Is Really Playing? Strategic Interaction in AI-Guided Populations
Jonathan Shaki, Eden Hartman, Sarit Kraus +1
AI systems in general, and Large language models (LLMs), in particular, are increasingly used to provide instructions to many agents who interact with one another. Such shared reli…
Ensemble Self-Training for Unsupervised Machine Translation
Ido Aharon, Jonathan Shaki, Sarit Kraus
We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT). Starting from a primary language pair, we train multiple UNMT models that…
Pro-AI Bias in Large Language Models
Benaya Trabelsi, Jonathan Shaki, Sarit Kraus
Large language models (LLMs) are increasingly employed for decision-support across multiple domains. We investigate whether these models display a systematic preferential bias in f…
Coalition Tactics: Bribery and Control in Parliamentary Elections
Hodaya Barr, Eden Hartman, Yonatan Aumann +1
Strategic manipulation of elections is typically studied in the context of promoting individual candidates. In parliamentary elections, however, the focus shifts: voters may care m…
Explaining Decentralized Multi-Agent Reinforcement Learning Policies
Kayla Boggess, Sarit Kraus, Lu Feng
Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, m…
Persuading Stable Matching
Jonathan Shaki, Jiarui Gan, Sarit Kraus
In bipartite matching problems, agents on two sides of a graph want to be paired according to their preferences. The stability of a matching depends on these preferences, which in…