22 citations · 41 across the 9 of their papers we have counts for
4 papers · 1 filter
Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
Kewei Cheng, Jingfeng Yang, Haoming Jiang +9
Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), mo…
IterAlign: Iterative Constitutional Alignment of Large Language Models
Xiusi Chen, Hongzhi Wen, Sreyashi Nag +5
With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcem…
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Tianxin Wei, Bowen Jin, Ruirui Li +8
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Ou…
Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions
Yinglun Xu, Tarun Suresh, Rohan Gumaste +10
Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework where one app…