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CLMN: Concept based Language Models via Neural Symbolic Reasoning
Yibo Yang
Deep learning has advanced NLP, but interpretability remains limited, especially in healthcare and finance. Concept bottleneck models tie predictions to human concepts in vision, b…
Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
Wu Fei, Shuxian Liang, Yibo Yang +5
Process Reinforcement Learning~(PRL) has demonstrated considerable potential in enhancing the reasoning capabilities of Large Language Models~(LLMs). However, introducing additiona…
Optimization-Inspired Few-Shot Adaptation for Large Language Models
Boyan Gao, Xin Wang, Yibo Yang +1
Large Language Models (LLMs) have demonstrated remarkable performance in real-world applications. However, adapting LLMs to novel tasks via fine-tuning often requires substantial t…
Inference Compute-Optimal Video Vision Language Models
Peiqi Wang, ShengYun Peng, Xuewen Zhang +5
This work investigates the optimal allocation of inference compute across three key scaling factors in video vision language models: language model size, frame count, and the numbe…
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
Boyan Gao, Bo Zhao, Shreyank N Gowda +4
Dataset condensation aims to synthesize datasets with a few representative samples that can effectively represent the original datasets. This enables efficient training and produce…