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Found 8 papers, 5 papers with code
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and reflection in MLLMs, due to the absence of substantial high-quality multimodal reasoning data.
Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification
To address this problem, we proposed a dynamic vision-language context sparsification framework Dynamic-LLaVA, which dynamically reduces the redundancy of vision context in the prefill stage and decreases the memory and computation overhead of the generated language context during decoding.
Ranked #133 on
Visual Question Answering
on MM-Vet
MoDification: Mixture of Depths Made Easy
Long-context efficiency has recently become a trending topic in serving large language models (LLMs).
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
This paper proposes a benchmark called DetectBench for verifying the ability to detect and piece together implicit evidence within a long context.
AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior
Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies.
Towards the Law of Capacity Gap in Distilling Language Models
Language model (LM) distillation is a trending area that aims to distil the knowledge residing in a large teacher LM to a small student one.
Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models
We introduces the DetectBench, a reading comprehension dataset designed to assess a model's ability to jointly ability in key information detection and multi-hop reasoning when facing complex and implicit information.
Action based Network for Conversation Question Reformulation
Conversation question answering requires the ability to interpret a question correctly.
