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Found 17 papers, 10 papers with code
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification
Finally, our approach achieves strong performance when combining MM-Reasoner and MM-Verifier, reaching an accuracy of 65. 3 on MathVista, surpassing GPT-4o (63. 8) with 12 rollouts.
Baichuan-M1: Pushing the Medical Capability of Large Language Models
The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like medicine, remain relatively scarce.
Baichuan-Omni-1.5 Technical Report
We introduce Baichuan-Omni-1. 5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities.
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains.
MathScape: Evaluating MLLMs in multimodal Math Scenarios through a Hierarchical Benchmark
To address this gap, we proposed MathScape, a new benchmark that emphasizes the understanding and application of combined visual and textual information.
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite.
SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models
Crucially, our method's reliance on purely generated data ensures the preservation of privacy, achieving SoTA performance with just 100k data points (only 18% of the official dataset size).
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important.
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
By integrating sensibility and rationality data with a MoE structure, we achieve even higher performance, demonstrating the effectiveness of our Efficient-Empathy algorithm.
Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning
Large language models equipped with retrieval-augmented generation (RAG) represent a burgeoning field aimed at enhancing answering capabilities by leveraging external knowledge bases.
Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation
To substantiate our hypothesis, we systematically analyze the performance of distillation methods by varying the model size of student models, the complexity of text, and the difficulty of decoding procedure.
Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory
However, the rationality information within the conversation is restricted, and previous methods of extending knowledge are subject to semantic conflict and single-role view.
Unraveling Key Factors of Knowledge Distillation
Knowledge distillation, a technique for model compression and performance enhancement, has gained significant traction in Neural Machine Translation (NMT).
Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review
Focusing on the application scenarios of decoding text and speech from brain signals in human-computer interaction, this paper presents a comprehensive review of the brain-inspired computing models based on deep learning (DL), tracking its evolution, application value, challenges and potential research trends.
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Remarkably, we show that even smaller base models, when equipped with our proposed approach, can achieve results comparable to those of larger models, illustrating the potential of our approach in harnessing the power of rationales for improved multimodal reasoning.
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A Survey on Image-text Multimodal Models
With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered widespread attention.
Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework
Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks.
