LLaVA-o1: Consentire ai Modelli di Linguaggio Visivo di Ragionare Passo dopo Passo

LLaVA-o1: Let Vision Language Models Reason Step-by-Step

November 15, 2024
Autori: Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, Li Yuan
cs.AI

Abstract

I grandi modelli linguistici hanno dimostrato significativi progressi nelle capacità di ragionamento, in particolare attraverso la scalabilità al momento dell'inferenza, come illustrato da modelli come l'o1 di OpenAI. Tuttavia, attualmente i Modelli Visione-Linguaggio (VLM) spesso faticano a eseguire un ragionamento sistematico e strutturato, specialmente quando affrontano compiti complessi di domande e risposte visive. In questo lavoro, presentiamo LLaVA-o1, un nuovo VLM progettato per condurre un ragionamento autonomo a più fasi. A differenza della semplice concatenazione di prompt, LLaVA-o1 si impegna autonomamente in fasi sequenziali di riassunto, interpretazione visiva, ragionamento logico e generazione di conclusioni. Questo approccio strutturato consente a LLaVA-o1 di ottenere notevoli miglioramenti nella precisione su compiti intensivi di ragionamento. Per raggiungere questo obiettivo, abbiamo compilato il dataset LLaVA-o1-100k, integrando campioni da varie fonti di domande e risposte visive e fornendo annotazioni di ragionamento strutturato. Inoltre, proponiamo un metodo di ricerca a fasci a livello di fase al momento dell'inferenza, che consente una scalabilità efficace al momento dell'inferenza. Notevolmente, con soli 100k campioni di addestramento e un metodo di scalabilità al momento dell'inferenza semplice ma efficace, LLaVA-o1 non solo supera il suo modello base del 8.9% su una vasta gamma di benchmark di ragionamento multimodale, ma supera anche le prestazioni di modelli più grandi e persino chiusi, come Gemini-1.5-pro, GPT-4o-mini e Llama-3.2-90B-Vision-Instruct.
English
Large language models have demonstrated substantial advancements in reasoning capabilities, particularly through inference-time scaling, as illustrated by models such as OpenAI's o1. However, current Vision-Language Models (VLMs) often struggle to perform systematic and structured reasoning, especially when handling complex visual question-answering tasks. In this work, we introduce LLaVA-o1, a novel VLM designed to conduct autonomous multistage reasoning. Unlike chain-of-thought prompting, LLaVA-o1 independently engages in sequential stages of summarization, visual interpretation, logical reasoning, and conclusion generation. This structured approach enables LLaVA-o1 to achieve marked improvements in precision on reasoning-intensive tasks. To accomplish this, we compile the LLaVA-o1-100k dataset, integrating samples from various visual question answering sources and providing structured reasoning annotations. Besides, we propose an inference-time stage-level beam search method, which enables effective inference-time scaling. Remarkably, with only 100k training samples and a simple yet effective inference time scaling method, LLaVA-o1 not only outperforms its base model by 8.9% on a wide range of multimodal reasoning benchmarks, but also surpasses the performance of larger and even closed-source models, such as Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct.

Summary

AI-Generated Summary

Paper Overview

The paper "On pre-training for visual language models" presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition in 2024 focuses on pre-training methods for visual language models. It emphasizes the significance of pre-training in enhancing model performance and efficiency, providing detailed insights into methodologies, experimental setups, and key findings.

Core Contribution

The core contribution lies in the introduction of LLaVA-o1, a Vision-Language Model (VLM) designed for autonomous multistage reasoning. It surpasses larger models with structured reasoning processes, introduces the LLaVA-o1-100k dataset, and proposes a stage-level beam search method for effective scaling during inference.

Research Context

The research positions itself within the field of visual language models, addressing challenges in existing models related to systematic and structured reasoning processes. It builds upon related works in visual reasoning with large language models and explores the use of Chain-of-Thought prompting for step-by-step reasoning trajectories.

Keywords

Pre-training, Visual Language Models, LLaVA-o1, Multistage Reasoning, Inference Time Scaling, Structured Reasoning, Stage-level Beam Search

Background

The research background of this paper delves into the importance of pre-training for visual language models to enhance their performance and efficiency. It addresses specific gaps in existing literature related to structured reasoning processes and inference time scaling methods.

Research Gap

Existing literature lacks systematic and structured reasoning processes in visual language models, necessitating the development of models like LLaVA-o1. Additionally, there is a need for effective inference time scaling methods to improve reasoning capabilities.

Technical Challenges

Technical challenges include the lack of structured reasoning processes in current models, hindering their performance in reasoning-intensive tasks. Moreover, efficient inference time scaling methods are crucial for enhancing model scalability and performance.

Prior Approaches

Prior approaches have focused on visual reasoning with large language models but have not adequately addressed the need for structured reasoning processes. The use of Chain-of-Thought prompting has shown promise in enhancing step-by-step reasoning trajectories.

Methodology

The research methodology involves establishing a theoretical foundation for LLaVA-o1, designing a technical architecture for multistage reasoning, implementing specific algorithms and tools, and highlighting innovation points for technical advantages.

Theoretical Foundation

LLaVA-o1 is based on a structured reasoning process that includes sequential stages of summarization, visual interpretation, logical reasoning, and conclusion generation. It utilizes supervised fine-tuning to enhance autonomous, stage-by-stage reasoning capabilities.

Technical Architecture

The technical architecture of LLaVA-o1 involves the creation of the LLaVA-o1-100k dataset with detailed reasoning annotations for training. It also incorporates a stage-level beam search method for effective inference time scaling and improved performance reliability.

Implementation Details

The implementation details include the use of structured tags to enhance model performance, facilitating reasoning processes. LLaVA-o1 demonstrates notable improvements in reasoning-intensive tasks like instance reasoning, logical reasoning, math, and science & technology.

Innovation Points

LLaVA-o1 introduces structured reasoning processes, the LLaVA-o1-100k dataset, and the stage-level beam search method, showcasing exceptional performance on reasoning tasks, scalability, and superiority over larger models.

Experimental Validation

The experimental validation involves setting up exact configurations, parameters, and datasets, defining metrics for evaluation, presenting quantitative and qualitative results, and conducting a comparative analysis with baseline methods.

Setup

The experimental setup includes training the LLaVA-o1 model on the LLaVA-o1-100k dataset to enhance reasoning capabilities. Inference time scaling using stage-level beam search is employed to improve the model's reasoning ability.

Metrics

Metrics for evaluation focus on reasoning-intensive benchmarks, comparing LLaVA-o1 with baseline methods like best-of-N and sentence-level beam search. Increasing the number of candidate responses in stage-level beam search consistently improves model performance.

Results

Experimental results demonstrate that LLaVA-o1 outperforms the base model in various benchmarks, showcasing its superiority in structured reasoning and scalability. Ablation studies highlight the effectiveness of the LLaVA-o1-100k dataset and structured tags in enhancing model performance.

Comparative Analysis

Comparative analysis shows that LLaVA-o1 surpasses state-of-the-art open-source and closed-source vision language models in reasoning-intensive benchmarks, establishing a new standard for multimodal reasoning with robust performance and scalability.

Impact and Implications

The impact and implications of the research include key findings on exceptional performance in reasoning tasks, limitations in certain aspects, future research opportunities, and practical significance in real-world applications.

Key Findings

LLaVA-o1 demonstrates exceptional performance on reasoning tasks, scalability with stage-level beam search, and superiority over larger models like Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct.

Limitations

While LLaVA-o1 shows significant improvements, there may be limitations in certain scenarios or tasks that require further exploration. Future research directions could address these limitations.

Future Directions

Future research opportunities include exploring external verifiers and reinforcement learning for enhancing multimodal reasoning capabilities further. These directions can contribute to advancing the field of visual language models.

Practical Significance

The practical significance of LLaVA-o1 lies in its ability to improve reasoning-intensive tasks in various domains like instance reasoning, logical reasoning, math, and science & technology. It offers concrete real-world applications in enhancing reasoning processes.

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