activity
20242026
most citedSystematic Biases in LLM Simulations of Debates

42 citations · 51 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Verifiable Social Reasoning for LLM Assistants

Amir Taubenfeld, Zorik Gekhman, Avigail Grinstein-Dabush +6

LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where t…

cs.CL2026

Evaluating Alignment of Behavioral Dispositions in LLMs

Amir Taubenfeld, Zorik Gekhman, Lior Nezry +8

As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral dispositions: the underlying tendenc…

cs.CL2025★ 9 cited

Confidence Improves Self-Consistency in LLMs

Amir Taubenfeld, Tom Sheffer, Eran Ofek +4

Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally e…

cs.CL2024

SAUCE: Synchronous and Asynchronous User-Customizable Environment for Multi-Agent LLM Interaction

Shlomo Neuberger, Niv Eckhaus, Uri Berger +3

Many human interactions, such as political debates, are carried out in group settings, where there are arbitrarily many participants, each with different views and agendas. To expl…

cs.CL2024★ 42 cited

Systematic Biases in LLM Simulations of Debates

Amir Taubenfeld, Yaniv Dover, Roi Reichart +1

The emergence of Large Language Models (LLMs), has opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. Current…