6 citations · 25 across the 20 of their papers we have counts for
14 papers · 1 filter
StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
Zefang Liu, Chenyang Zhu, Sangwoo Cho +3
Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We p…
ReHear: Iterative Pseudo-Label Refinement for Semi-Supervised Speech Recognition via Audio Large Language Models
Zefang Liu, Chenyang Zhu, Sangwoo Cho +1
Semi-supervised learning in automatic speech recognition (ASR) typically relies on pseudo-labeling, which often suffers from confirmation bias and error accumulation due to noisy s…
Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models
Zefang Liu, Nam H. Nguyen, Yinzhu Quan +1
Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level r…
AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding
Arman Anwar, Zefang Liu
Traditional cybersecurity tabletop exercises (TTXs) provide valuable training but are often scripted, resource-intensive, and difficult to scale. We introduce AgentBnB, a browser-b…
AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation
Zefang Liu, Arman Anwar
Incident response (IR) requires fast, coordinated, and well-informed decision-making to contain and mitigate cyber threats. While large language models (LLMs) have shown promise as…
EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments
Zefang Liu, Yinzhu Quan
We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The benchmark comprises 360 curated tas…
Multi-Agent Collaboration in Incident Response with Large Language Models
Zefang Liu
Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively. Leveraging large language…
Retrieval of Temporal Event Sequences from Textual Descriptions
Zefang Liu, Yinzhu Quan
Retrieving temporal event sequences from textual descriptions is crucial for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking cr…
InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
Yinzhu Quan, Zefang Liu
Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently. Effective inventory manage…
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
Yinzhu Quan, Zefang Liu
In this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms o…
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts
Zefang Liu, Jiahua Luo
We introduce AdaMoLE, a novel method for fine-tuning large language models (LLMs) through an Adaptive Mixture of Low-Rank Adaptation (LoRA) Experts. Moving beyond conventional meth…
SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Zefang Liu
In this paper, we introduce SecQA, a novel dataset tailored for evaluating the performance of Large Language Models (LLMs) in the domain of computer security. Utilizing multiple-ch…
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches
Zefang Liu, John Buford
Anomaly detection in command shell sessions is a critical aspect of computer security. Recent advances in deep learning and natural language processing, particularly transformer-ba…
Yelp Review Rating Prediction: Machine Learning and Deep Learning Models
Zefang Liu
We predict restaurant ratings from Yelp reviews based on Yelp Open Dataset. Data distribution is presented, and one balanced training dataset is built. Two vectorizers are experime…