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20232026
most citedModel Compression and Efficient Inference for Large Language Models: A Survey

14 citations · 23 across the 14 of their papers we have counts for

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6 papers · 1 filter

cs.CL2024

Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control

Yuxin Xiao, Chaoqun Wan, Yonggang Zhang +5

As the development and application of Large Language Models (LLMs) continue to advance rapidly, enhancing their trustworthiness and aligning them with human preferences has become…

cs.CL2024★ 1 cited

SciPIP: An LLM-based Scientific Paper Idea Proposer

Wenxiao Wang, Lihui Gu, Liye Zhang +7

The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases:…

cs.CV2024

Depth Any Video with Scalable Synthetic Data

Honghui Yang, Di Huang, Wei Yin +6

Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introd…

cs.CV2024

Adapt2Reward: Adapting Video-Language Models to Generalizable Robotic Rewards via Failure Prompts

Yanting Yang, Minghao Chen, Qibo Qiu +5

For a general-purpose robot to operate in reality, executing a broad range of instructions across various environments is imperative. Central to the reinforcement learning and plan…

cs.AI2024

AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning

Minghao Chen, Yihang Li, Yanting Yang +3

Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agent…

cs.CL2024★ 14 cited

Model Compression and Efficient Inference for Large Language Models: A Survey

Wenxiao Wang, Wei Chen, Yicong Luo +6

Transformer based large language models have achieved tremendous success. However, the significant memory and computational costs incurred during the inference process make it chal…