link: https://arxiv.org/abs/2503.16022 Mario Sanz-Guerrero¹ and Katharina von der Wense¹,² (¹Johannes Gutenberg University Mainz, Germany; ²University of Colorado Boulder, USA) This paper introduces and evaluates "Corrective In-Context Learning" (CICL), a novel approach intended to improve in-context learning in large language models by incorporating self-correction
link: https://arxiv.org/abs/2407.13690 Authors: Yuxuan Tong (Tsinghua University), Xiwen Zhang (Helixon Research), Rui Wang (Helixon Research), Ruidong Wu (Helixon Research), Junxian He (HKUST) Introduction Mathematical reasoning remains one of the most challenging domains for large language models (LLMs). Despite recent advances, even state-of-the-art models struggle with
link: https://arxiv.org/abs/2503.16212 Paper by: Qizhi Pei, Lijun Wu, Zhuoshi Pan, Yu Li, Honglin Lin, Chenlin Ming, Xin Gao, Conghui He, Rui Yan Introduction Mathematical reasoning remains a critical benchmark for assessing the cognitive capabilities of Large Language Models (LLMs). While significant progress has been made
link: https://arxiv.org/pdf/2502.02533v1 Han Zhou¹ ², Xingchen Wan¹, Ruoxi Sun¹, Hamid Palangi¹, Shariq Iqbal¹, Ivan Vulić¹ ², Anna Korhonen² and Sercan Ö. Arık¹ ¹Google, ²University of Cambridge I'd be happy to convert this academic paper into a dialogue. I'll create a lab meeting discussion
Mirac Suzgun (Stanford University), Adam Tauman Kalai (OpenAI) Lab Meeting Discussion (1) A: Good morning everyone! Today we have a presentation from our colleague who will be discussing their paper on meta-prompting. Please take it away. (2) Author: Thank you for having me today. I'm excited to share
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng - Microsoft (1) A: Good morning everyone! Today we have a presentation from Reid on his team's recent work on automatic prompt optimization. As usual, Reid will present for about 7 minutes, and then we&
Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang, Zhijian Ma, Bingchen Qian, Fei Wei, Wenhao Zhang, Yuexiang Xie, Daoyuan Chen, Liuyi Yao, Hongyi Peng, Zeyu Zhang, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou - Alibaba Group Lab Meeting Transcript (1) A: Welcome everyone to today&
Yang Yan, Yu Lu, Lizhi Ma, Zhenzhong Lan link: https://openreview.net/forum?id=sdpVfWOUQA Planning with MCTS: Enhancing Problem-Solving in Large Language Models Planning with MCTS: Enhancing Problem-Solving in Large Language Models Anonymous Authors from Various Research Institutions Part 1: Author's Summary (1) A: Good afternoon, everyone!
By Miao Li, Michael Klamkin, Mathieu Tanneau, Reza Zandehshahvar, and Pascal Van Hentenryck (Georgia Institute of Technology) link: https://www.arxiv.org/abs/2503.04071 Part 1: Author's Summary (1) A: Welcome everyone to today's lab meeting. We have a presentation from the authors of "
Michael Krumdick, Charles Lovering, Varshini Reddy, Seth Ebner, Chris Tanner (Kensho Technologies, MIT) link:https://arxiv.org/abs/2503.05061 (1) A: Good morning, everyone! Today we have Michael Krumdick presenting his recent paper, "No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding." Michael, the floor is yours
Ziqian Zhong, Jacob Andreas - Massachusetts Institute of Technology link:https://proceedings.neurips.cc/paper_files/paper/2024/file/bccdd196d798a51a4961989984a9ed4a-Paper-Conference.pdf (1) A: Welcome everyone to today's lab meeting. We're excited to have Ziqian Zhong presenting their paper "Algorithmic Capabilities of Random Transformers," which
Jingtong Su (NYU & Meta AI, FAIR), Julia Kempe (NYU & Meta AI, FAIR), Karen Ullrich (Meta AI, FAIR) link: https://proceedings.neurips.cc/paper_files/paper/2024/file/439bf902de1807088d8b731ca20b0777-Paper-Conference.pdf (1) A: Good afternoon, everyone! Today we have a presentation from the author about their recent paper titled "