Transformer language models like GPT-4 and ChatGPT have demonstrated remarkable capabilities across a wide range of tasks, sparking both admiration and concern about their potential impact. However, a recent paper titled "Faith and Fate: Limits of Transformers on Compositionality" by researchers from Allen Institute for AI, University of Washington, University of Southern California and University of Chicago takes a critical look at the limitations of these models in tasks requiring multi-step compositional reasoning. …
Voyager is the first LLM (Large Language Models) powered embodied lifelong learning agent that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. The agent is designed to operate in the Minecraft environment, a popular open-ended game that offers a rich set of tasks and interactions. …
Reflexion is a novel framework proposed by Shinn et al. for reinforcing language agents through linguistic feedback rather than traditional weight updates. The key idea is to have agents verbally reflect on feedback signals, maintain the reflective text in an episodic memory buffer, and use this to guide better decision making in subsequent trials. …
Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models (LLMs). "Scaling Laws for Fine-Grained Mixture of Experts", Jakub Krajewski, Jan Ludziejewski, and their colleagues from the University of Warsaw and IDEAS NCBR analyze the scaling properties of MoE models, incorporating an expanded range of variables. …
Large Language Models (LLMs) like GPT-4, ChatGPT, and J1-Jumbo have revolutionized natural language processing, enabling unprecedented performance on a wide range of tasks. However, the high cost of querying these LLM APIs is a major barrier to their widespread adoption, especially for high-throughput applications. …
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications. However, no single model can optimally address all tasks, especially when considering the trade-off between performance and cost. This has led to the development of LLM routing systems that leverage the strengths of various models. …
Neural networks often exhibit a puzzling phenomenon called "polysemanticity" where many unrelated concepts are packed into a single neuron, making interpretability challenging. This paper provides toy models to understand polysemanticity as a result of models storing additional sparse features in "superposition". Key findings include: …
Cognitive Architectures for Language Agents: A Framework for Building Intelligent Language Models. Large language models (LLMs) have achieved impressive results on many natural language tasks. However, to build truly intelligent agents, we need to equip LLMs with additional capabilities like memory, reasoning, learning, and interacting with the environment. A new paper titled "Cognitive Architectures for Language Agents" proposes a framework called CoALA to guide the development of such language agents. …
Retrieval-Augmented Generation (RAG) has emerged as a promising solution to enhance Large Language Models (LLMs) by incorporating knowledge from external databases. This survey paper provides a comprehensive examination of the progression of RAG paradigms, including Naive RAG, Advanced RAG, and Modular RAG. …
Large Language Models (LLMs) like ChatGPT have transformed numerous fields by leveraging their extensive reasoning and generalization capabilities. However, as the complexity of prompts increases, with techniques like chain-of-thought (CoT) and in-context learning (ICL) becoming more prevalent, the computational demands skyrocket. This paper introduces LLMLingua, a sophisticated prompt compression method designed to mitigate these challenges. By compressing prompts into a more compact form without significant loss of semantic integrity, LLMLingua enables faster inference and reduced computational costs, promising up to 20x compression rates with minimal performance degradation. …
The paper introduces a novel approach to optimize memory usage in serving Large Language Models (LLMs) through a method called PagedAttention, inspired by virtual memory and paging techniques in operating systems. This method addresses the significant memory waste in existing systems due to inefficient handling of key-value (KV) cache memory, which is crucial for the performance of LLMs. …
The field of large language models (LLMs) has witnessed a paradigm shift with the advent of model merging, a novel approach that combines multiple LLMs into a unified architecture without additional training, offering a cost-effective strategy for new model development. This technique has sparked a surge in experimentation due to its potential to democratize the development of foundational models. However, the reliance on human intuition and domain knowledge in model merging has been a limiting factor, calling for a more systematic method to explore new model combinations. …
Training Large Language Models (LLMs) presents significant memory challenges predominantly due to the growing size of weights and optimizer states. While common memory-reduction approaches, such as Low-Rank Adaptation (LoRA), have been employed to mitigate these challenges, they typically underperform training with full-rank weights in both pre-training and fine-tuning stages. This limitation arises because these approaches restrict the parameter search to a low-rank subspace, altering training dynamics and potentially requiring a full-rank warm start. …
A team of researchers has released OpenMoE, a series of open-source Mixture-of-Experts (MoE) based large language models ranging from 650M to 34B parameters. Their work provides valuable insights into training MoE models and analyzing their behavior. Here are some key takeaways: …
Reframing Large Language Models (LLMs) as agents has ushered in a new paradigm of automation. Researchers and practitioners have increasingly been using these models as agents to automate complex tasks using specialized functions. However, integrating useful functions into LLM agents often requires manual effort and extensive iterations, which is time-consuming and inefficient. Inspired by the analogy of humans continuously forging tools to adapt to tasks, this paper introduces a novel approach to train LLM agents by forging their functions, treating them as learnable 'agent parameters', without modifying the LLM weights. This paradigm, termed 'Agent Training', involves updating the agent's functions to maximize task-solving ability, offering a promising avenue for developing specialized LLM agents efficiently. …
Abstract: Large Language Models (LLMs) drive significant advancements in AI, yet understanding their internal workings remains a challenge. This paper introduces a novel geometric perspective to characterize LLMs, offering practical insights into their functionality. By analyzing the intrinsic dimension of Multi-Head Attention (MHA) embeddings and the affine mappings within layer feed-forward networks, we unlock new ways to manipulate and interpret LLMs. Our findings enable bypassing restrictions like RLHF in models such as Llama2, and we introduce seven interpretable spline features extracted from any LLM layer. These features, tested on models like Mistral-7B and Llama2, prove highly effective in toxicity detection, domain inference, and addressing the Jigsaw challenge, showcasing the practical utility of our geometric characterization. …
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons: …
When fine-tuning Large Language Models (LLMs) like GPT-3 or BERT for specific tasks, a common challenge encountered is "forgetting" – where the model loses some of its pre-trained capabilities. This phenomenon is particularly noticeable in Parameter-Efficient Fine-Tuning (PEFT) methods such as Low-Rank Adapters (LoRA). …
Large language models (LLMs) are cornerstone technologies in AI, driving advancements across various fields. However, the traditional approach of re-training LLMs with every new data set is both costly and computationally inefficient. This paper presents a novel approach, focusing on continual pre-training, which allows for the incremental updating of LLMs without the need for full re-training, significantly saving computational resources. …
Low-Rank Adapters (LoRA) have emerged as a popular parameter-efficient fine-tuning method for large language models. By adding trainable low-rank "adapters" to selected layers, LoRA enables effective fine-tuning while dramatically reducing the number of parameters that need to be trained. However, the conventional LoRA method uses a scaling factor for the adapters that divides them by the rank. A new paper by researcher Damjan Kalajdzievski shows that this rank-dependent scaling actually slows down learning and limits performance improvements when using higher-rank adapters. …
The exponential growth of large language models poses significant challenges in terms of deployment costs and environmental impact due to high energy consumption. In response to these challenges, this paper introduces BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. By introducing BitLinear as a replacement for the traditional nn.Linear layer, BitNet aims to train with 1-bit weights from scratch, significantly reducing the memory footprint and energy consumption while maintaining competitive performance. …
The realm of artificial intelligence has witnessed a significant breakthrough with the introduction of the SELF-DISCOVER framework, a novel approach that empowers Large Language Models (LLMs) to autonomously uncover and employ intrinsic reasoning structures. This advancement is poised to redefine how AI systems tackle complex reasoning challenges, offering a more efficient and interpretable method compared to traditional prompting techniques. …
In the ever-evolving landscape of artificial intelligence, a groundbreaking development emerges with "Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution." This paper introduces an innovative approach that pushes the boundaries of how Large Language Models (LLMs) can be enhanced, not through manual tweaks but via an evolutionary mechanism that refines the art of prompting itself. …
Google has just introduced Gemma, an innovative family of state-of-the-art open Large Language Models (LLMs), marking a significant stride in the open-source AI landscape. This release, featuring both 7B and 2B parameter models, underscores Google's ongoing commitment to open-source AI. The Hugging Face team is thrilled to support this launch, ensuring seamless integration within our ecosystem. …
The paper "A Decoder-Only Foundation Model for Time-Series Forecasting" introduces a groundbreaking approach in the field of time-series forecasting, leveraging the power of decoder-only models, commonly used in natural language processing, to achieve remarkable zero-shot forecasting capabilities across a variety of domains. …
A key challenge has been improving these models beyond a certain point, especially without the continuous infusion of human-annotated data. A groundbreaking paper by Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu presents an innovative solution: Self-Play Fine-Tuning (SPIN). …
The application of Socratic methods to LLMs like GPT-4 can significantly enhance their ability to process and interpret complex inquiries. Here's how some of these prompt templates can be applied: …
Chang's paper revolves around the Socratic method, a technique rooted in critical thinking and inquiry through dialogue. The paper identifies and adapts various Socratic techniques such as definition, elenchus, dialectic, maieutics, generalization, induction, and counterfactual reasoning. These techniques are ingeniously applied to improve interactions with GPT-3, aiming to produce more accurate, concise, and creative outputs. …
The paper explores the innovative application of Large Language Models (LLMs) in corporate planning, particularly in developing sales strategies. It proposes that LLMs can significantly enhance the value-driven sales process. …
The landscape of deep learning is continually evolving, and a recent groundbreaking development comes from the world of sequence modeling. A paper titled "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" introduces a novel approach that challenges the current dominance of Transformer-based models. Let's delve into this innovation. …
Language models (LMs) have been making remarkable strides in understanding and generating human language. Yet, their true potential in problem-solving tasks has been somewhat limited by the reliance on human-generated data. The groundbreaking paper, "Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models", introduces a novel method named Reinforced Self-Training (ReST) that promises to change this landscape. …
In the dynamic world of Artificial Intelligence (AI), the realm of Reinforcement Learning (RL) has witnessed a paradigm shift, brought to the forefront by the groundbreaking paper "Deep Reinforcement Learning from Human Preferences". This novel approach, straying from the traditional pathways of predefined reward functions, paves the way for a more intuitive and human-centric method of training RL agents. Let's dive into the intricacies and implications of this innovative research. …
In the realm of artificial intelligence, the confluence of visual and language data represents a groundbreaking shift. The Large Language and Vision Assistant (LLaVA) model exemplifies this evolution. Unlike traditional AI models, LLaVA integrates visual inputs with linguistic context, offering a more holistic understanding of both textual and visual data. …
Orca 2 marks a significant advancement in language model development, emphasizing enhanced reasoning abilities in smaller models. This blog explores Orca 2's innovative methodologies, "Cautious Reasoning" and "Prompt Erasing," detailing their impact on AI language modeling. …
In the ever-evolving landscape of technology, the fusion of artificial intelligence with software development has opened new horizons. The paper "A Survey on Language Models for Code" provides a comprehensive overview of this fascinating evolution. From the early days of statistical models to the sophisticated era of Large Language Models (LLMs) and Transformers, the journey of code processing models has been nothing short of revolutionary. …
This blog post delves into the key concepts of "System 2 Attention" (S2A) mechanism, introduced in a recent paper by Jason Weston and Sainbayar Sukhbaatar from Meta, its implementation, and the various variations explored in the paper. …
The paper "Let’s Verify Step by Step" from OpenAI presents an insightful exploration into the training of large language models (LLMs) for complex multi-step reasoning tasks. Focusing on mathematical problem-solving, the authors investigate the efficacy of process supervision versus outcome supervision in training more reliable models. …
EcoAssistant, built on the principles outlined in the paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately", showcases an advanced application of AutoGen in AI-driven question answering. The system's implementation hinges on three pivotal features: …
In the ever-evolving landscape of artificial intelligence, the recent paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately" emerges as a groundbreaking study. This research paper delves into the complexities of utilizing Large Language Models (LLMs) in a cost-effective and accurate manner, specifically for code-driven question answering. This innovation builds on the capabilities of Autogen, a key component in enhancing the effectiveness of the model. …
AutoGen is an open-source framework that facilitates the development of LLM (Large Language Model) applications using a multi-agent conversation approach. It allows developers to build customizable, conversable agents capable of operating in various modes, combining LLMs, human inputs, and tools. …
The recent advancement in AI, dubbed MemGPT, marks a significant leap in the capabilities of Large Language Models (LLMs). Developed by a team at UC Berkeley, MemGPT addresses a critical challenge in LLMs: managing extended context for complex tasks. This blog delves into the groundbreaking features of MemGPT, illustrating how it could reshape our interaction with conversational AI and document analysis. …
The research paper "A Survey on Large Language Model based Autonomous Agents" from Renmin University of China presents a detailed overview of the advancements in the field of autonomous agents driven by Large Language Models (LLMs). This paper provides insights into various aspects of agent architecture, including profiling, memory, planning, and action modules, along with their applications, evaluation strategies, and future directions. …
We've taken on the exciting challenge of implementing the cutting-edge strategies presented in "ZEPHYR: Direct Distillation of LM Alignment". This paper's approach is not just theoretical—it's a blueprint for a significant leap in language model training. By adopting ZEPHYR's distilled direct preference optimization (dDPO), we've embarked on a code journey that brings these innovations from concept to reality. …
In today's rapidly growing world of conversational AI, developers often seek ways to leverage multiple models seamlessly to diversify outputs and enhance user experience. One such scenario involves using different Local Language Models (LLMs) to serve different purposes or to offer a variety of responses. In this article, we'll explore a method to set up and switch between multiple local LLMs, particularly Zephyr and Mistral 7B, using the Chainlit and Langchain libraries. …
Model fine-tuning and quantization play pivotal roles in creating efficient and robust machine learning solutions. This blog post explores the fine-tuning process of the Zephyr 7B GPT-Q model using 4-bit quantization to boost its performance for custom data inference tasks. …
In today's post, we delve into a recent paper that investigates the intricacies of Reinforcement Learning in the context of Large Language Models (LLMs). This study shines a light on the challenges and nuances of training such models to align better with human preferences. …
We've all been there - diligently using Proximal Policy Optimization (PPO) for text generation, only to wonder if there's more to be extracted from our models. If you've been in this boat, you're in for a treat! A recent paper under review for ICLR 2024 offers some intriguing insights. …
The paper proposes a new technique called "Constitutional AI" (CAI) to train AI systems like chatbots to be helpful, honest, and harmless without needing human feedback labels identifying harmful behaviors. Instead, the training relies entirely on AI-generated feedback guided by simple principles. This makes it possible to control AI behavior more precisely with far less human input. …
In the rapidly evolving landscape of large language models (LLMs), enhancing their capabilities and performance is pivotal. Three prominent techniques that stand out in achieving this are: …
Large language models (LLMs) like GPT-3 offer impressive text generation capabilities. But with API pricing tied to compute usage, heavy costs limit wider adoption of LLMs. How can we maximize the value extracted from these models under budget constraints? …
The world of machine learning has been witnessing monumental growth, powered by the scaling of models. "Scaling Laws for Autoregressive Generative Modeling" is a pivotal paper in this context, offering profound insights into the mechanics of this scaling. This blog post distills the paper's essence for a clearer understanding. …
In the realm of machine learning, large language models have transformed our capabilities. However, decoding these behemoths efficiently remains a challenge. Enter Speculative Sampling, a technique that promises to revolutionize this decoding process. …
In the vast realm of machine learning, fine-tuning stands out as one of the most crucial techniques for adapting pre-trained models to new tasks. Ludwig, a deep learning toolkit, offers a diverse palette of fine-tuning strategies that cater to different needs. In this blog, we'll delve into these techniques, especially focusing on the Quantization-Based Fine-Tuning (QLoRA) method, as we explore the Code Alpaca project's efforts in instruction-based code generation using LLaMA models. …
Machine learning enthusiasts and researchers are constantly advancing the frontiers of technology, crafting larger and more sophisticated models, especially in the domain of Natural Language Processing (NLP). However, not all of us have the resources to run these behemoths. If you've ever been curious about running the more manageable, smaller counterparts of some of the most prominent language models on your own computer, then this blog post offers the perfect insight! …
In the AI realm, language models are paramount. From revolutionizing chatbots to pioneering content generation, they've altered our machine interaction landscape. But like all great innovations, challenges persist. As these models burgeon in sophistication, so does their memory appetite, making their pivotal optimization process, fine-tuning, a pricey endeavor. That's where QLORA steps in, heralding a new era for Large Language Models (LLMs). …
The world of Natural Language Processing (NLP) has been buzzing with the advancements in large language models. One such intriguing development is the Low-Rank Adaptation (LoRA) technique. In this blog post, we'll dive deep into the intricacies of low-rank updates, shedding light on the empirical advantages and the underlying principles of using pre-trained models for downstream tasks. …
In the tapestry of technological wonders that envelops our world, the study "Discovering Insights Beyond the Known: A Dialogue Between GPT-4 Agents from Adam and Eve to the Nexus of Ecology, AI, and the Brain" embarks on an enlightening journey, engaging in a dialogue that traverses the intersections of AI, human intuition, and uncharted creativity. Authored by Edward Y. Chang and Emily J. Chang, the paper unfurls a captivating exploration of interdisciplinary landscapes—from the biblical origin of Adam and Eve to the intricate crossroads of ecology, AI, and the human psyche. …