Summary of Honesty to Subterfuge: In-context Reinforcement Learning Can Make Honest Models Reward Hack, by Leo Mckee-reid et al.
Honesty to Subterfuge: In-Context Reinforcement Learning Can Make Honest Models Reward Hackby Leo McKee-Reid, Christoph…
Honesty to Subterfuge: In-Context Reinforcement Learning Can Make Honest Models Reward Hackby Leo McKee-Reid, Christoph…
Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRAby Maharshi Gor,…
Auto-Evolve: Enhancing Large Language Model’s Performance via Self-Reasoning Frameworkby Krishna Aswani, Huilin Lu, Pranav Patankar,…
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Databy Jeremy Andrew Irvin, Emily Ruoyu…
Leveraging free energy in pretraining model selection for improved fine-tuningby Michael Munn, Susan WeiFirst submitted…
Contrastive Learning to Improve Retrieval for Real-world Fact Checkingby Aniruddh Sriram, Fangyuan Xu, Eunsol Choi,…
Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Modelsby Xinyuan Wang, Haozhou…
Towards Linguistically-Aware and Language-Independent Tokenization for Large Language Models (LLMs)by Abrar Rahman, Garry Bowlin, Binit…
Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videosby Jianrui Zhang, Mu Cai, Yong…
Heuristics and Biases in AI Decision-Making: Implications for Responsible AGIby Payam Saeedi, Mahsa Goodarzi, M…