1 citations · 2 across the 4 of their papers we have counts for
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Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs
Panatchakorn Anantaprayoon, Nataliia Babina, Nima Asgharbeygi +1
LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable alternatives such as RL with AI f…
Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment
Panatchakorn Anantaprayoon, Nataliia Babina, Jad Tarifi +1
Large Language Models (LLMs) are typically aligned with human values using preference data or predefined principles such as helpfulness, honesty, and harmlessness. However, as AI s…
Intent-Aware Self-Correction for Mitigating Social Biases in Large Language Models
Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki
Self-Correction based on feedback improves the output quality of Large Language Models (LLMs). Moreover, as Self-Correction functions like the slow and conscious System-2 thinking…
Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels
Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki
Discriminatory gender biases have been found in Pre-trained Language Models (PLMs) for multiple languages. In Natural Language Inference (NLI), existing bias evaluation methods hav…