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  • arxiv preprint – Transformers need glasses! Information over-squashing in language tasks

    In this episode, we discuss Transformers need glasses! Information over-squashing in language tasks by Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João G. M. Araújo, Alex Vitvitskyi, Razvan Pascanu, Petar Veličković. The paper explores how information propagates in decoder-only Transformers, revealing a phenomenon where different input sequences can result in nearly identical final token…

  • arxiv preprint – Show, Don’t Tell: Aligning Language Models with Demonstrated Feedback

    In this episode, we discuss Show, Don’t Tell: Aligning Language Models with Demonstrated Feedback by Omar Shaikh, Michelle Lam, Joey Hejna, Yijia Shao, Michael Bernstein, Diyi Yang. The paper introduces Demonstration ITerated Task Optimization (DITTO), a method for customizing language model outputs using fewer than ten demonstrations as feedback. DITTO, based on online imitation learning,…

  • arxiv preprint – TextGrad: Automatic ”Differentiation” via Text

    In this episode, we discuss TextGrad: Automatic “Differentiation” via Text by Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, James Zou. The paper introduces TEXTGRAD, a novel framework that automates the optimization of compound AI systems by utilizing textual feedback from large language models (LLMs). TEXTGRAD treats text feedback as a…

  • arxiv preprint – SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales

    In this episode, we discuss SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales by Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, Jing Gao. The paper introduces SaySelf, a framework for training large language models (LLMs) to produce accurate, fine-grained confidence estimates and self-reflective rationales explaining their uncertainties. This is…

  • arxiv preprint – Open-Endedness is Essential for Artificial Superhuman Intelligence

    In this episode, we discuss Open-Endedness is Essential for Artificial Superhuman Intelligence by Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktaschel. The paper argues that the development of open-ended, self-improving AI systems is achievable using current foundation models trained on extensive internet data. It provides a formal…

  • arxiv preprint – To Believe or Not to Believe Your LLM

    In this episode, we discuss To Believe or Not to Believe Your LLM by Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári. The study investigates uncertainty quantification in large language models (LLMs), focusing on distinguishing large epistemic uncertainty to identify unreliable outputs and potential hallucinations. By employing an information-theoretic metric and a method of…

  • arxiv preprint – Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts

    In this episode, we discuss Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts by Chunjing Gan, Dan Yang, Binbin Hu, Hanxiao Zhang, Siyuan Li, Ziqi Liu, Yue Shen, Lin Ju, Zhiqiang Zhang, Jinjie Gu, Lei Liang, Jun Zhou. The paper introduces METRAG, a novel Multi-layered Thought enhanced Retrieval-Augmented Generation…

  • arxiv preprint – Contextual Position Encoding: Learning to Count What’s Important

    In this episode, we discuss Contextual Position Encoding: Learning to Count What’s Important by Olga Golovneva, Tianlu Wang, Jason Weston, Sainbayar Sukhbaatar. The paper introduces Contextual Position Encoding (CoPE), a new position encoding method for Large Language Models (LLMs) that incrementally alters position based on context rather than just token count. This approach enables more…

  • arxiv preprint – Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

    In this episode, we discuss Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis by Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rongrong…

  • arxiv preprint – VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos

    In this episode, we discuss VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos by Ziyang Wang, Shoubin Yu, Elias Stengel-Eskin, Jaehong Yoon, Feng Cheng, Gedas Bertasius, Mohit Bansal. The paper introduces VideoTree, a novel framework that enhances the efficiency and accuracy of long-video question answering by selectively extracting and hierarchically organizing frames…

  • arxiv preprint – CinePile: A Long Video Question Answering Dataset and Benchmark

    In this episode, we discuss CinePile: A Long Video Question Answering Dataset and Benchmark by Ruchit Rawal, Khalid Saifullah, Ronen Basri, David Jacobs, Gowthami Somepalli, Tom Goldstein. CinePile is a new dataset and benchmark designed for authentic long-form video understanding, addressing the limitations of current datasets. It comprises 305,000 multiple-choice questions (MCQs) spanning various visual…

  • arxiv preprint – Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum

    In this episode, we discuss Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum by Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Oncel Tuzel. The paper introduces a novel variable sequence length training technique called dataset decomposition to address inefficiencies in training large language models (LLMs)…

  • arxiv preprint – SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

    In this episode, we discuss SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering by John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, Ofir Press. The paper introduces SWE-agent, an autonomous system leveraging a language model to tackle software engineering tasks through a specialized agent-computer interface (ACI). SWE-agent significantly improves task completion…

  • arxiv preprint – Octo: An Open-Source Generalist Robot Policy

    In this episode, we discuss Octo: An Open-Source Generalist Robot Policy by Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, Sergey Levine. The paper introduces Octo, a…

  • arxiv preprint – Layer-Condensed KV Cache for Efficient Inference of Large Language Models

    In this episode, we discuss Layer-Condensed KV Cache for Efficient Inference of Large Language Models by Haoyi Wu, Kewei Tu. The paper addresses the significant memory consumption issue in deploying large language models by proposing a novel method that computes and caches key-value pairs for only a small number of layers, thereby saving memory and…

  • arxiv preprint – Observational Scaling Laws and the Predictability of Language Model Performance

    In this episode, we discuss Observational Scaling Laws and the Predictability of Language Model Performance by Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto. The paper introduces an observational approach to building scaling laws for language models by utilizing approximately 80 publicly available models, bypassing the need for extensive model training. It discovers that despite variations…

  • arxiv preprint – Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization

    In this episode, we discuss Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization by Costas Mavromatis, Petros Karypis, George Karypis. The paper presents PackLLM, a method for fusing knowledge from multiple Large Language Models (LLMs) during test-time by optimizing the importance of each LLM based on the input prompt to minimize perplexity. It…

  • arxiv preprint – The Platonic Representation Hypothesis

    In this episode, we discuss The Platonic Representation Hypothesis by Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola. The paper argues that representations in AI models, particularly deep networks, are converging across various domains and data modalities. This convergence suggests a movement towards a shared statistical model of reality, termed the “platonic representation.” The authors…

  • arxiv preprint – Many-Shot In-Context Learning in Multimodal Foundation Models

    In this episode, we discuss Many-Shot In-Context Learning in Multimodal Foundation Models by Yixing Jiang, Jeremy Irvin, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H. Chen, Andrew Y. Ng. The paper examines the effectiveness of increased example capacities in multimodal foundation models’ context windows to advance in-context learning (ICL). It specifically looks at the transition…

  • arxiv preprint – Naturalistic Music Decoding from EEG Data via Latent Diffusion Models

    In this episode, we discuss Naturalistic Music Decoding from EEG Data via Latent Diffusion Models by Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano, Akima Connelly, Emanuele Rodolà, Taketo Akama. The paper explores the use of latent diffusion models to decode complex musical compositions from EEG data, focusing on music that includes varied instruments and vocal harmonics.…

  • arxiv preprint – The Chosen One: Consistent Characters in Text-to-Image Diffusion Models

    In this episode, we discuss The Chosen One: Consistent Characters in Text-to-Image Diffusion Models by Omri Avrahami, Amir Hertz, Yael Vinker, Moab Arar, Shlomi Fruchter, Ohad Fried, Daniel Cohen-Or, Dani Lischinski. The paper introduces a novel method for creating character images that remain consistent in various settings using text-to-image diffusion models. It details a technique…

  • arxiv preprint – Memory Mosaics

    In this episode, we discuss Memory Mosaics by Jianyu Zhang, Niklas Nolte, Ranajoy Sadhukhan, Beidi Chen, Léon Bottou. Memory Mosaics are collective networks designed for prediction tasks, utilizing associative memories in a collaborative manner. These networks offer a simpler and more transparent alternative to transformers, maintaining comparable abilities in compositional learning and learning in context.…

  • arxiv preprint – Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

    In this episode, we discuss Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? by Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, Jonathan Herzig. The paper explores the effects of integrating new factual information into large language models (LLMs) during the fine-tuning phase, particularly focusing on how this affects their ability…

  • arxiv preprint – LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

    In this episode, we discuss LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models by Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia. The abstract describes “LongLoRA,” a technique designed to efficiently expand the context size of large language models (LLMs) while maintaining computational feasibility. This methodology includes a novel…

  • arxiv preprint – WildChat: 1M ChatGPT Interaction Logs in the Wild

    In this episode, we discuss WildChat: 1M ChatGPT Interaction Logs in the Wild by Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, Yuntian Deng. WILDCHAT is a dataset featuring 1 million user-ChatGPT conversations with over 2.5 million interaction turns, created by collecting chat transcripts and request headers from users who consented to participate.…