The field of multimodal reasoning and large language models is moving towards more innovative and efficient approaches. Researchers are exploring new methods to improve the performance of large language models, such as using adaptive planning graphs, tailored teaching with balanced difficulty, and structured solution templates. These approaches aim to enhance the models' ability to reason and integrate information from diverse sources, such as images and texts. Noteworthy papers include: MMAPG, which proposes a training-free framework for multimodal multi-hop question answering via adaptive planning graphs. Do Cognitively Interpretable Reasoning Traces Improve LLM Performance, which investigates the relationship between cognitively interpretable reasoning traces and LLM performance. Tailored Teaching with Balanced Difficulty, which proposes a novel framework for elevating reasoning in multimodal chain-of-thought via prompt curriculum. Can Structured Templates Facilitate LLMs in Tackling Harder Tasks, which explores the use of structured templates to facilitate LLMs in tackling harder tasks.
Advancements in Multimodal Reasoning and Large Language Models
Sources
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs
Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
Reasoning Steps as Curriculum: Using Depth of Thought as a Difficulty Signal for Tuning LLMs
Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum
Beyond the Textual: Generating Coherent Visual Options for MCQs
Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Beyond Shallow Heuristics: Leveraging Human Intuition for Curriculum Learning