30 citations · 33 across the 7 of their papers we have counts for
8 papers
CoMem: Reusing Transformer Depth across Queries with Persistent Intermediate Residuals
Hanzuo Liu, Xuan Qi, Chunyu Liu +6
Repeated queries over shared documents repeatedly execute the same lower transformer layers. We introduce CoMem, which makes split depth j an explicit reusable-context axis: write…
From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation
Zhi Chen, Minmao Wang, Xingchen Liu +8
Semantic-ID-based generative recommenders enable efficient next-item generation, but their item-level supervision mainly captures behavioral co-occurrence and local transitions. La…
SparseForge: Efficient Semi-Structured LLM Sparsification via Annealing of Hessian-Guided Soft-Mask
Liu Hanzuo, Chaofan Lin, Weixuan Sun +4
Semi-structured sparsity provides a practical path to accelerate large language models (LLMs) with native hardware support, but post-training semi-structured pruning often suffers…
Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
Tianjie Ju, Yiting Wang, Xinbei Ma +7
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solvi…
Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning
Yulong Wang, Tianhao Shen, Lifeng Liu +1
Existing agents based on large language models (LLMs) demonstrate robust problem-solving capabilities by integrating LLMs' inherent knowledge, strong in-context learning and zero-s…
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
Ang Lv, Yuhan Chen, Kaiyi Zhang +5
In this paper, we delve into several mechanisms employed by Transformer-based language models (LLMs) for factual recall tasks. We outline a pipeline consisting of three major steps…
On the Robustness of Editing Large Language Models
Xinbei Ma, Tianjie Ju, Jiyang Qiu +4
Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation…
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
Zhuosheng Zhang, Hanqing Zhang, Keming Chen +4
Although pre-trained models (PLMs) have achieved remarkable improvements in a wide range of NLP tasks, they are expensive in terms of time and resources. This calls for the study o…