Papers by Amir Taubenfeld
Confidence Improves Self-Consistency in LLMs (2025.findings-acl)
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| Challenge: | Modern large language models (LLMs) demonstrate strong reasoning capabilities, driven in part by their capacity to generate a sequence of intermediate reasoning steps that lead them toward a final answer. |
| Approach: | They propose a method that performs a weighted majority vote based on confidence scores obtained directly from the model. |
| Outcome: | The proposed method outperforms self-consistency on nine models and four datasets, reducing the required number of reasoning paths by over 40% on average. |
Systematic Biases in LLM Simulations of Debates (2024.emnlp-main)
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| Challenge: | Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as substitutes for human participants in behavioral studies. |
| Approach: | They propose to use LLMs to simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes. |
| Outcome: | The proposed model can simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes. |