1 citations · 2 across the 10 of their papers we have counts for
10 papers
DeLog: An Efficient Log Compression Framework with Pattern Signature Synthesis
Siyu Yu, Yifan Wu, Junjielong Xu +8
Parser-based log compression, which separates static templates from dynamic variables, is a promising approach to exploit the unique structure of log data. However, its performance…
Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space
Xiang Zhang, Kun Wei, Xu Yang +3
As Large Language Models (LLMs) become increasingly prevalent, their security vulnerabilities have already drawn attention. Machine unlearning is introduced to seek to mitigate the…
Tokenization Constraints in LLMs: A Study of Symbolic and Arithmetic Reasoning Limits
Xiang Zhang, Juntai Cao, Jiaqi Wei +2
Tokenization is the first - and often underappreciated - layer of computation in language models. While Chain-of-Thought (CoT) prompting enables transformer models to approximate r…
ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis with Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities
Xiang Zhang, Chenlin Xu, Zhouxiao Lu +2
Quantifying similarity between population spike patterns is essential for understanding how neural dynamics encode information. Traditional approaches, which combine kernel smoothi…
Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs
Xiang Zhang, Juntai Cao, Jiaqi Wei +2
Despite the remarkable successes of large language models (LLMs), the underlying Transformer architecture has inherent limitations in handling complex reasoning tasks. Chain-of-tho…
Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing
Juntai Cao, Xiang Zhang, Raymond Li +4
Recent advances in test-time scaling have shown promising results in improving Large Language Model (LLM) performance through strategic computation allocation during inference. Whi…
Counting Ability of Large Language Models and Impact of Tokenization
Xiang Zhang, Juntai Cao, Chenyu You
Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Tran…
Supervised Chain of Thought
Xiang Zhang, Dujian Ding
Large Language Models (LLMs) have revolutionized natural language processing and hold immense potential for advancing Artificial Intelligence. However, the core architecture of mos…
Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databases
Xiang Zhang, Khatoon Khedri, Reza Rawassizadeh
Large Language Models (LLMs) can automate or substitute different types of tasks in the software engineering process. This study evaluates the resource utilization and accuracy of…
From Discrete to Continuous: Deep Fair Clustering With Transferable Representations
Xiang Zhang
We consider the problem of deep fair clustering, which partitions data into clusters via the representations extracted by deep neural networks while hiding sensitive data attribute…