7 papers
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
Zheng Li, Qingxiu Dong, Jingyuan Ma +3
Recently, large reasoning models demonstrate exceptional performance on various tasks. However, reasoning models always consume excessive tokens even for simple queries, leading to…
Decoding in Geometry: Alleviating Embedding-Space Crowding for Complex Reasoning
Yixin Yang, Qingxiu Dong, Zhifang Sui
Sampling-based decoding underlies complex reasoning in large language models (LLMs), where decoding strategies critically shape model behavior. Temperature- and truncation-based me…
Beyond Single Frames: Can LMMs Comprehend Temporal and Contextual Narratives in Image Sequences?
Xiaochen Wang, Heming Xia, Jialin Song +9
Large Multimodal Models (LMMs) have achieved remarkable success across various visual-language tasks. However, existing benchmarks predominantly focus on single-image understanding…
How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation
Rui Li, Heming Xia, Xinfeng Yuan +4
Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and auto…
RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection
Yixin Yang, Qingxiu Dong, Linli Yao +2
Data selection for instruction tuning is crucial for improving the performance of large language models (LLMs) while reducing training costs. In this paper, we propose Refined Cont…
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey
Liang Chen, Zekun Wang, Shuhuai Ren +24
Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning ta…