AI Breakdown

arxiv preprint - Future Lens: Anticipating Subsequent Tokens from a Single Hidden State

In this episode, we discuss Future Lens: Anticipating Subsequent Tokens from a Single Hidden State by Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace, David Bau. The paper investigates if single hidden state vectors from an input token in a model like GPT-J-6B can predict multiple future tokens in a sequence. Using linear approximation and causal intervention methods, the researchers found that certain layers allow accurate future token prediction from a single hidden state with over 48% accuracy. They introduce "Future Lens," a visualization tool that leverages their findings to give a new perspective on transformer states and their predictive capabilities.

arxiv preprint - Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

In this episode, we discuss Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity by Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park. The paper introduces an adaptive QA model that optimizes the balance between efficiency and accuracy by choosing the appropriate response strategy for questions of varying complexity. A smaller language model classifies the question's complexity level, enabling the system to switch between different retrieval-augmented LLM strategies and even non-retrieval methods. The model outperforms existing baselines on various open-domain QA datasets, and the authors have made the code available on GitHub.

arxiv preprint - Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

In this episode, we discuss Mixture-of-Depths: Dynamically allocating compute in transformer-based language models by David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, Adam Santoro. The study presents a method for transformers that allows for the dynamic allocation of computational resources within sequences by limiting the number of tokens processed at each layer using a top-k routing mechanism. This approach maintains a fixed tensor size and a static computation graph, which differs from other conditional computation strategies. The resulting model operates with fewer computations per forward pass and provides up to a 50% faster sampling rate post-training, while still matching the performance of baseline models with the same computational budget and training duration.

arxiv preprint - WavLLM: Towards Robust and Adaptive Speech Large Language Model

In this episode, we discuss WavLLM: Towards Robust and Adaptive Speech Large Language Model by Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei. The paper introduces WavLLM, a robust speech large language model with a unique dual-encoder system—one for semantic content and another for speaker identity—enhanced by a two-stage curriculum learning approach and a prompt-aware weight adapter for flexible task handling. WavLLM excels at a broad range of speech-processing tasks such as ASR, ST, SV, ER, and SQA, demonstrating state-of-the-art performance and strong generalization across various contexts. Resources related to the model, including codes and evaluation sets, have been made available for further research.

arxiv preprint - Gecko: Versatile Text Embeddings Distilled from Large Language Models

In this episode, we discuss Gecko: Versatile Text Embeddings Distilled from Large Language Models by Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, Yi Luan, Sai Meher Karthik Duddu, Gustavo Hernandez Abrego, Weiqiang Shi, Nithi Gupta, Aditya Kusupati, Prateek Jain, Siddhartha Reddy Jonnalagadda, Ming-Wei Chang, Iftekhar Naim. Gecko is a new text embedding model designed for efficient retrieval, using a novel two-step knowledge distillation process from large language models. First, it creates varied synthetic query-passage pairs, then it improves the data by selecting and relabeling high-quality candidates. Despite its smaller size, Gecko demonstrates superior retrieval performance, outpacing larger models with higher dimensionality on a benchmark test.

arxiv preprint - ReALM: Reference Resolution As Language Modeling

In this episode, we discuss ReALM: Reference Resolution As Language Modeling by Joel Ruben Antony Moniz, Soundarya Krishnan, Melis Ozyildirim, Prathamesh Saraf, Halim Cagri Ates, Yuan Zhang, Hong Yu, Nidhi Rajshree. This paper presents a method for using Large Language Models (LLMs) to resolve references, including complex ones such as entities on a user's screen or in the background, by framing reference resolution as a language modeling task. The proposed system shows significant improvements, with over 5% gains in handling on-screen references, compared to an existing system. Moreover, the paper reports that even the smallest model within their framework performs comparably to GPT-4, while their larger models outperform GPT-4.

arxiv preprint - sDPO: Don’t Use Your Data All at Once

In this episode, we discuss sDPO: Don't Use Your Data All at Once by Dahyun Kim, Yungi Kim, Wonho Song, Hyeonwoo Kim, Yunsu Kim, Sanghoon Kim, Chanjun Park. The paper introduces stepwise DPO (sDPO), a novel technique for better aligning large language models (LLM) with human preferences by utilizing preference datasets in stages rather than all at once. sDPO improves upon the direct preference optimization (DPO) process by employing progressively aligned reference models throughout training. The results showed that models trained using sDPO outperformed larger, more parameter-heavy LLMs, demonstrating the effectiveness of this stepwise approach.

arxiv preprint - LITA: Language Instructed Temporal-Localization Assistant

In this episode, we discuss LITA: Language Instructed Temporal-Localization Assistant by De-An Huang, Shijia Liao, Subhashree Radhakrishnan, Hongxu Yin, Pavlo Molchanov, Zhiding Yu, Jan Kautz. The paper introduces the Language Instructed Temporal-Localization Assistant (LITA), which tackles the issue of temporal localization in Large Language Models (LLMs) processing video content, where they struggle to identify "when" an event occurs in a video. LITA incorporates time tokens for better temporal representation, uses a SlowFast token architecture for finer temporal resolution, and emphasizes training on temporal localization data, introducing a new task with its dataset (ActivityNet-RTL). The implementation of LITA demonstrates strong performance improvements in temporal localization tasks and video-based text generation, with the code available on GitHub for public use.

arxiv preprint - AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks

In this episode, we discuss AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks by Max Ku, Cong Wei, Weiming Ren, Harry Yang, Wenhu Chen. This paper presents AnyV2V, a framework that simplifies video-to-video editing by breaking it down into two main steps. It leverages existing image editing models to edit individual frames and then uses a temporally coherent transformation to create a new video. The framework is designed to be versatile and does not require any additional training, allowing it to meet a broad range of user requirements for video editing.

arxiv preprint - InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding

In this episode, we discuss InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding by Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Jilan Xu, Zun Wang, Yansong Shi, Tianxiang Jiang, Songze Li, Hongjie Zhang, Yifei Huang, Yu Qiao, Yali Wang, Limin Wang. InternVideo2 is a cutting-edge video foundation model designed to understand and generate video content, achieving superior performance across multiple video and audio tasks. The training involves a progressive strategy that combines multiple learning techniques and emphasizes the connection between video and text, enhanced through semantic segmentation and the generation of captions. The model's capabilities were proven through rigorous testing, displaying exceptional proficiency in video captioning, dialogue, and understanding of extended video sequences.