Category: Uncategorized

In this episode we discuss A Survey on Large Language Model based Autonomous Agents by Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen. The authors of this paper conducted a comprehensive survey on the topic of…

In this episode we discuss Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies by Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, William Yang Wang. The paper provides a comprehensive review of self-correction strategies for large language models (LLMs). It examines recent work on self-correction techniques, categorizing them into…

In this episode we discuss Rethinking the Expressive Power of GNNs via Graph Biconnectivity by Bohang Zhang, Shengjie Luo, Liwei Wang, Di He. This paper introduces a new approach called Generalized Distance Weisfeiler-Lehman (GD-WL) to study the expressive power of Graph Neural Networks (GNNs). The authors show that most existing GNN architectures are not expressive…

In this episode we discuss Conditional Antibody Design as 3D Equivariant Graph Translation by Xiangzhe Kong, Wenbing Huang, Yang Liu. The paper introduces a method called Multi-channel Equivariant Attention Network (MEAN) for antibody design. MEAN addresses challenges faced by existing deep-learning-based methods by formulating antibody design as a conditional graph translation problem and incorporating additional…

In this episode we discuss ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation by Xuefeng Hu, Ke Zhang, Lu Xia, Albert Chen, Jiajia Luo, Yuyin Sun, Ken Wang, Nan Qiao, Xiao Zeng, Min Sun, Cheng-Hao Kuo, Ram Nevatia. The paper discusses ReCLIP, a source-free domain adaptation method for large-scale pre-training vision-language models…

In this episode we discuss LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition by Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, Min Lin. The paper presents LoraHub, a framework for combining Low-rank adaptations (LoRA) to improve cross-task generalization in fine-tuning large language models (LLMs). LoraHub allows the assembly of LoRA modules…

In this episode we discuss Emergence of Maps in the Memories of Blind Navigation Agents by Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra. The paper explores whether blind artificial intelligence agents can develop implicit maps of their environment. The study involves training these agents in navigation tasks and finding…

In this episode we discuss On the duality between contrastive and non-contrastive self-supervised learning by Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, Yann Lecun. This paper discusses the duality between contrastive and non-contrastive self-supervised learning methods for image representations. It highlights the theoretical similarities between these approaches and introduces algebraically related contrastive and covariance-based…

In this episode we discuss LISA: Reasoning Segmentation via Large Language Model by Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, Jiaya Jia. The paper introduces a new segmentation task called reasoning segmentation and presents a benchmark dataset for evaluating models. They propose LISA, a model that combines language generation with…

In this episode we discuss Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP by Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, Liang-Chieh Chen. The paper proposes a single-stage framework for open-vocabulary segmentation using a shared Frozen Convolutional CLIP (FC-CLIP) backbone. FC-CLIP simplifies the pipeline and achieves a better accuracy-cost trade-off compared to…

In this episode we discuss PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization by Junhyeong Cho, Gilhyun Nam, Sungyeon Kim, Hunmin Yang, Suha Kwak. The paper introduces a method called PromptStyler for domain generalization in a joint vision-language space. It achieves this by synthesizing diverse styles using prompts without using any images. The method learns…

In this episode we discuss Extrapolating Large Language Models to Non-English by Aligning Languages by Wenhao Zhu, Yunzhe Lv, Qingxiu Dong, Fei Yuan, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li. The paper proposes a method to improve the language abilities of large language models (LLMs) in non-English languages. They achieve this by…

In this episode we discuss Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching by Donggyun Kim, Jinwoo Kim, Seongwoong Cho, Chong Luo, Seunghoon Hong. The paper proposes Visual Token Matching (VTM), a few-shot learning solution for arbitrary dense prediction tasks in computer vision. VTM uses non-parametric matching on patch-level embedded tokens of…

In this episode we discuss Generalization on the Unseen, Logic Reasoning and Degree Curriculum by Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk. This paper examines the performance of different network architectures trained by stochastic gradient descent (SGD) in the generalization on the unseen (GOTU) setting. The authors find that certain network models, such as…

In this episode we discuss Gorilla: Large Language Model Connected with Massive APIs by Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez. The paper introduces Gorilla, a fine-tuned Large Language Model (LLM) that excels in generating accurate API calls. By combining Gorilla with a document retriever, the model exhibits the ability to adapt…

In this episode we discuss Learning-Rate-Free Learning by D-Adaptation by Aaron Defazio, Konstantin Mishchenko. The paper introduces D-Adaptation, a learning-rate-free approach for setting the learning rate in convex minimization problems. It achieves the optimal rate of convergence without additional evaluations per step. The method is shown to match hand-tuned learning rates in diverse machine learning…

In this episode we discuss Shepherd: A Critic for Language Model Generation by Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, Sean O’Brien, Ramakanth Pasunuru, Jane Dwivedi-Yu, Olga Golovneva, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz. The paper introduces Shepherd, a language model trained to critique responses generated by large language models (LLMs) and offer suggestions for…

In this episode we discuss Adapting to game trees in zero-sum imperfect information games by Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko. The paper presents two Follow the Regularized Leader (FTRL) algorithms for learning ε-optimal strategies in zero-sum imperfect information games (IIGs). Players have uncertainty about the true game state,…

In this episode we discuss Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning by Zeyuan Allen-Zhu, Yuanzhi Li. The paper explores how ensembles of deep learning models can improve test accuracy and be distilled into a single model using knowledge distillation. It presents a theoretical framework that shows how ensembles can enhance test…

In this episode we discuss Exploring Format Consistency for Instruction Tuning by Shihao Liang, Kunlun Zhu, Runchu Tian, Yujia Qin, Huadong Wang, Xin Cong, Zhiyuan Liu, Xiaojiang Liu, Maosong Sun. The paper investigates the impact of format inconsistency on the performance of instruction tuning and proposes a framework called “Unified Instruction Tuning” (UIT) that utilizes…

In this episode we discuss DreamFusion: Text-to-3D using 2D Diffusion by Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall. The paper presents DREAMFUSION, a method that uses a pretrained 2D text-to-image diffusion model to synthesize 3D objects from text. By optimizing a randomly-initialized 3D model using gradient descent and a loss based on probability…

In this episode we discuss A Watermark for Large Language Models by John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein. This paper presents a watermarking framework for large language models (LLMs), aiming to embed hidden signals in the generated text while remaining undetectable to humans. The approach involves selecting specific tokens…

In this episode we discuss Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding by Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang. The paper proposes a method called “Skeleton-of-Thought” (SoT) to decrease the generation latency of large language models (LLMs). The sequential decoding approach used in current LLMs contributes to high latency. SoT…

In this episode we discuss Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning by Anton Bakhtin, David J Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H Miller, Noam Brown. The paper introduces a strategy called DiL-piKL that combines human imitation learning with reinforcement learning and planning to…

In this episode we discuss RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment by Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian. The paper presents a method called Reinforcement Learning from Contrast Distillation (RLCD) for aligning language models to natural language principles. RLCD trains a preference model using simulated preference pairs…