AI Breakdown

arxiv preprint - Block-State Transformers

In this episode we discuss Block-State Transformers
by Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin. The paper introduces the Block-State Transformer (BST) architecture that merges state space models and block-wise attention to effectively capture long-range dependencies and improve performance on language modeling tasks. The BST incorporates an SSM sublayer for long-range context and a Block Transformer sublayer for local sequence processing, enhancing parallellization and combining the strengths of both model types. Experiments demonstrate the BST's superior performance over traditional Transformers in terms of perplexity, generalization to longer sequences, and a significant acceleration in processing speed due to model parallelization.

arxiv preprint - Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns

In this episode we discuss Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns
by Brian DuSell, David Chiang. The paper introduces stack attention, a novel attention mechanism that incorporates the concept of stacks to help recognize hierarchical and nested syntactic structures, which traditional scaled dot-product attention fails to handle effectively. Two versions of stack attention are presented, one deterministic and one nondeterministic, both aiming to enhance transformers' ability to parse context-free languages (CFLs) without requiring explicit syntactic training data. Experimental results reveal that transformers equipped with stack attention outperform standard transformers on CFLs with complex parsing requirements and also show improvements in natural language modeling and machine translation within a limited parameter setting.

arxiv preprint - LooseControl: Lifting ControlNet for Generalized Depth Conditioning

In this episode we discuss LooseControl: Lifting ControlNet for Generalized Depth Conditioning
by Shariq Farooq Bhat, Niloy J. Mitra, Peter Wonka. LOOSECONTROL is introduced as a novel method for depth-conditioned image generation that is less reliant on detailed depth maps, unlike the state-of-the-art ControlNet. It allows for content creation by specifying scene boundaries or 3D box layouts for objects, which can then be refined using either 3D box editing or attribute editing techniques. The results of LOOSECONTROL outperform baselines, and with its potential as a design tool for creating complex scenes, the authors make their code and additional information available online.

Announcement: AI Breakdown Youtube Channel

Welcome back to AI Breakdown! In this special announcement, your hosts Megan and Ray share exciting news - we're expanding to YouTube! This new platform will add a visual dimension to our discussions, bringing AI papers to life with figures, tables, and results. While the podcast will continue as usual, the YouTube channel will offer a more immersive experience, perfect for those who prefer a visual approach to understanding AI. Stay tuned for this new chapter in AI Breakdown, and check out AI Breakdown YouTube Channel!

arxiv preprint - OneLLM: One Framework to Align All Modalities with Language

In this episode we discuss OneLLM: One Framework to Align All Modalities with Language
by Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, Xiangyu Yue. The paper introduces OneLLM, a multimodal large language model that unifies the encoding of eight different modalities to language via a single framework. It uses a new image projection module and a universal projection module for multimodal alignment, extending the model's capability to progressively align more modalities. OneLLM is demonstrated to excel in various multimodal tasks across 25 benchmarks and is supplementarily supported by a specially curated multimodal instruction dataset with 2 million items, with resources accessible online.

arxiv preprint - The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

In this episode we discuss The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
by Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, Nouha Dziri, Melanie Sclar, Khyathi Chandu, Chandra Bhagavatula, Yejin Choi. The paper discusses the effectiveness of traditional alignment tuning methods for large language models (LLMs) and introduces a new, simple tuning-free method named URIAL (Untuned LLMs with Restyled In-context ALignment). Analysis reveals that alignment tuning primarily adjusts the language style without significant transformation of the knowledge base, with the majority of decoding remaining identical to the base LLM. The proposed URIAL method, which utilizes strategic prompting and in-context learning with just a few stylistic examples, achieves comparable or superior performance to models aligned through traditional methods, questioning the necessity of complex alignment tuning and emphasizing the need for deeper understanding of LLM alignment.

arxiv - MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

In this episode, we discuss MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI by Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen. MMMU is a new benchmark for evaluating multimodal models using college-level questions from various disciplines to test advanced reasoning and subject knowledge. The benchmark contains 11.5K questions across six core disciplines and 30 subjects, featuring diverse visual content like graphs and music sheets. Initial testing on 14 models, including the sophisticated GPT-4V, showed a best accuracy of 56%, suggesting ample scope for improvement in artificial general intelligence.

arxiv preprint - MLP-Mixer: An all-MLP Architecture for Vision

In this episode we discuss MLP-Mixer: An all-MLP Architecture for Vision
by Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy. The paper presents MLP-Mixer, an architecture that relies solely on multi-layer perceptrons (MLPs) for image classification tasks, demonstrating that neither convolutions nor attention mechanisms are necessary for high performance. The MLP-Mixer operates with two types of layers: one that processes features within individual image patches, and another that blends features across different patches. The model achieves competitive results on benchmarks when trained on large datasets or with modern regularization techniques, suggesting a new direction for image recognition research beyond conventional CNNs and Transformers.

arxiv preprint - Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

In this episode we discuss Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
by Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, Eric Horvitz. The paper discusses enhancing the performance of GPT-4, a generalist language model, in medical question-answering tasks without domain-specific training. By innovatively engineering prompts, the researchers created Medprompt, which significantly outperformed specialized models, achieving state-of-the-art results on the MultiMedQA benchmark suite with fewer model calls. Moreover, Medprompt was also successful in generalizing its capabilities to other fields, demonstrating its broad applicability across various competency exams beyond medicine.

arxiv preprint - Nash Learning from Human Feedback

In this episode we discuss Nash Learning from Human Feedback by Remi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot from Google DeepMind. The paper introduces Nash Learning from Human Feedback (NLHF), a new approach for tuning large language models (LLMs) based on human preferences, different from the traditional reinforcement learning from human feedback (RLHF). The NLHF technique involves learning a preference model from paired comparisons and refining the LLM's policy towards a Nash equilibrium, where no alternative policy produces more preferred responses. They developed a Nash-MD algorithm and gradient descent approaches for implementing NLHF, and demonstrated its effectiveness on a text summarization task, suggesting NLHF as a promising direction for aligning LLMs with human preferences.