2 citations · 3 across the 5 of their papers we have counts for
6 papers
BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration
Chence Yang, Ningxi Cheng, Arash Akbari +9
Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, existing methods often requir…
Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization
Qingchan Zhu, Weihang You, Hanqi Jiang +3
Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask which tokens to keep. This retained-token view can keep redundant high-s…
ViThinker: Active Vision-Language Reasoning via Dynamic Perceptual Querying
Weihang You, Qingchan Zhu, David Liu +3
Chain-of-Thought (CoT) reasoning excels in language models but struggles in vision-language models due to premature visual-to-text conversion that discards continuous information s…
AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models
Qin Zhu, Fei Huang, Runyu Peng +6
While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are v…
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation
Qin Zhu, Qingyuan Cheng, Runyu Peng +5
The training process of large language models (LLMs) often involves varying degrees of test data contamination. Although current LLMs are achieving increasingly better performance…
Unified Active Retrieval for Retrieval Augmented Generation
Qinyuan Cheng, Xiaonan Li, Shimin Li +7
In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial…