4 accepted papers
While low-rank decomposition offers potential for LLM size reduction, its application is limited by considerable performance degradation. In this work, we identify and formalize a key overlooked issue in LLM decomposition: \textit{representation drift}. We show that approximation errors introduced b…
In long structured document retrieval, existing methods typically fine-tune pre-trained language models (PLMs) using contrastive learning on datasets lacking explicit structural information. This practice suffers from two critical issues: 1) current methods fail to leverage structural features and e
Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but the billion-scale parameters pose deployment challenges. Although existing methods attempt to reduce the scale of LLMs, they require either special hardware support or expensive post-training to maintain…
In recent years, reinforcement learning has been widely applied in the field of games. However, most studies focus on assisting agents to achieve victory, with less attention paid to whether the agents exhibit human-like characteristics. In order to build human-like agents with high performance, we…