Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study

• URL: http://arxiv.org/abs/2502.11514v1
• Date: Mon, 17 Feb 2025 07:29:01 GMT
• Title: Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study
• Authors: Yujie Lin, Ante Wang, Moye Chen, Jingyao Liu, Hao Liu, Jinsong Su, Xinyan Xiao,
• Abstract summary: We investigate popular sampling-based and tree search-based inference-time scaling methods on 10 challenging tasks spanning various domains. Results show that multi-modal thought promotes better performance against conventional text-only thought. Despite these advantages, multi-modal thoughts necessitate higher token consumption for processing richer visual inputs.
• Score: 44.35454088618666
• License:
• Abstract: Recently, inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks. While existing studies have predominantly centered on text-based thinking, the integration of both visual and textual modalities within the reasoning process remains unexplored. In this study, we pioneer the exploration of inference-time scaling with multi-modal thought, aiming to bridge this gap. To provide a comprehensive analysis, we systematically investigate popular sampling-based and tree search-based inference-time scaling methods on 10 challenging tasks spanning various domains. Besides, we uniformly adopt a consistency-enhanced verifier to ensure effective guidance for both methods across different thought paradigms. Results show that multi-modal thought promotes better performance against conventional text-only thought, and blending the two types of thought fosters more diverse thinking. Despite these advantages, multi-modal thoughts necessitate higher token consumption for processing richer visual inputs, which raises concerns in practical applications. We hope that our findings on the merits and drawbacks of this research line will inspire future works in the field.

Related papers

• Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs [86.79757571440082]
  Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable abilities in complex reasoning tasks. We identify a phenomenon we term underthinking, where o1-like LLMs frequently switch between different reasoning thoughts. We propose a decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts.
  arXiv  Detail & Related papers  (2025-01-30T18:58:18Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs) We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs. We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• Cross-Modal Consistency in Multimodal Large Language Models [33.229271701817616]
  We introduce a novel concept termed cross-modal consistency. Our experimental findings reveal a pronounced inconsistency between the vision and language modalities within GPT-4V. Our research yields insights into the appropriate utilization of such models and hints at potential avenues for enhancing their design.
  arXiv  Detail & Related papers  (2024-11-14T08:22:42Z)
• Interventional Imbalanced Multi-Modal Representation Learning via $β$-Generalization Front-Door Criterion [17.702549833449435]
  Multi-modal methods establish comprehensive superiority over uni-modal methods. In imbalanced contributions of different modalities to task-dependent predictions constantly degrade the discriminative performance of canonical multi-modal methods. Benchmark methods raise a tractable solution: augmenting the auxiliary modality with a minor contribution during training.
  arXiv  Detail & Related papers  (2024-06-17T12:55:56Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
  We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models. We aim for the models to engage with and generate responses that span a wider contextual scene understanding. Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
  arXiv  Detail & Related papers  (2024-03-20T11:27:20Z)
• Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought [70.30423016640749]
  Chain-of-thoughts (CoT) methods were proposed to guide large language models to reason step-by-step, enabling problem solving from simple to complex. The evaluation from the large language model (LLMs) is typically noisy and unreliable, potentially misleading the generation process in selecting promising intermediate thoughts. In this paper, motivated by Vapnik's principle, we use pairwise-comparison evaluation instead of point-wise scoring to search for promising intermediate thoughts.
  arXiv  Detail & Related papers  (2024-02-10T09:51:03Z)
• DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models [28.712359821231182]
  Large language models (LLMs) have made remarkable strides in such multi-step reasoning on the language modality solely by leveraging the chain of thought (CoT) to mimic human thinking. The transfer of these advancements to multimodal contexts introduces heightened challenges, including but not limited to the impractical need for labor-intensive annotation. This study proposes a novel DDCoT prompting that maintains a critical attitude through negative-space prompting and incorporates multimodality into reasoning.
  arXiv  Detail & Related papers  (2023-10-25T08:03:10Z)
• Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions [8.14509634354919]
  We consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The emerging paradigm of Evolutionary Multitasking tackles multitask optimization scenarios by using as inspiration concepts drawn from Evolutionary Computation.
  arXiv  Detail & Related papers  (2021-02-04T11:48:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.