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20192026
most citedA Survey on Large Language Model based Autonomous Agents

1.6k citations · 5.9k across the 102 of their papers we have counts for

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57 papers · 1 filter

cs.CL2026

Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models

Jia Deng, Junyi Li, Wayne Xin Zhao +3

Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training methods largely rely on random…

cs.CL2026

FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents

Jia Deng, Yimeng Chen, Xiaoqing Xiang +9

Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. Existing synthesis methods of…

cs.CL2023★ 1 cited

Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment

Geyang Guo, Ranchi Zhao, Tianyi Tang +2

Alignment with human preference is a desired property of large language models (LLMs). Currently, the main alignment approach is based on reinforcement learning from human feedback…

cs.CL2023★ 98 cited

Large Language Models for Information Retrieval: A Survey

Yutao Zhu, Huaying Yuan, Shuting Wang +7

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve…

cs.CL2023★ 1 cited

Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

Peiyu Liu, Zikang Liu, Ze-Feng Gao +5

Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…

cs.CL2023★ 19 cited

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

Ruiyang Ren, Yuhao Wang, Yingqi Qu +6

Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge. However, it remains unclear how well LLMs are able to perceive the…