28 citations · 97 across the 33 of their papers we have counts for
5 papers · 1 filter
LLMs Can Get "Brain Rot": A Pilot Study on Twitter/X
Shuo Xing, Junyuan Hong, Yifan Wang +5
We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To unveil junk effects, we…
SEAL: Steerable Reasoning Calibration of Large Language Models for Free
Runjin Chen, Zhenyu Zhang, Junyuan Hong +2
Large Language Models (LLMs), such as OpenAI's o1-series have demonstrated compelling capabilities for complex reasoning tasks via the extended chain-of-thought (CoT) reasoning mec…
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More
Xialie Zhuang, Zhikai Jia, Jianjin Li +4
Large Language Models (LLMs) are discovered to suffer from accurately retrieving key information. To address this, we propose Mask-Enhanced Autoregressive Prediction (MEAP), a simp…
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
Zhenyu Zhang, Runjin Chen, Shiwei Liu +5
This paper aims to overcome the "lost-in-the-middle" challenge of large language models (LLMs). While recent advancements have successfully enabled LLMs to perform stable language…
Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention
Zhen Tan, Tianlong Chen, Zhenyu Zhang +1
Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains. However, the enigmatic ``black-box'' nature of LLMs remains a…