1 citations · 1 across the 9 of their papers we have counts for
11 papers
Who May Release an Agent's Promise? Standing over Commitments in Multi-Party Chat
Lan Wei, Bingyu Shen, Jiaqi Tang +6
When an assistant serving a team makes a promise to Alice, who may cancel it? We call this standing: only the creditor (the person owed) or someone the creditor or operator policy…
MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs
Boyang Li, Bingyu Shen, Weihao Hong +6
Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization weakens alignment safeguards, lea…
Lacuna: A Research Map for Machine Learning
Martin Weiss, Miles Q. Li, Alejandro H. Artiles +4
Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research directions, and research prop…
Adaptive Prompt Embedding Optimization for LLM Jailbreaking
Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2
Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the prompt and operates in a combi…
LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models
Zhiyuan Jiang, Weihao Hong, Xinlei Guan +8
Vision-Language Models (VLMs) are increasingly deployed in settings where reliable visual grounding carries operational consequences, yet their behavior under progressively coerciv…
Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents
Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2
The rapid deployment of LLM-based autonomous agents has introduced safety risks that extend far beyond traditional LLM concerns, prompting a proliferation of safety benchmarks sinc…
A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents
Miles Q. Li, Benjamin C. M. Fung, Martin Weiss +3
As autonomous AI agents are increasingly deployed in high-stakes environments, ensuring their safety and alignment with human values is becoming a practical deployment concern. Cur…
Security Concerns for Large Language Models: A Survey
Miles Q. Li, Benjamin C. M. Fung
Large Language Models (LLMs) such as ChatGPT and its competitors have caused a revolution in natural language processing, but their capabilities also introduce new security vulnera…
Training Dynamics of a 1.7B LLaMa Model: A Data-Efficient Approach
Miles Q. Li, Benjamin C. M. Fung, Shih-Chia Huang
Pretraining large language models is a complex endeavor influenced by multiple factors, including model architecture, data quality, training continuity, and hardware constraints. I…
On the Effectiveness of Incremental Training of Large Language Models
Miles Q. Li, Benjamin C. M. Fung, Shih-Chia Huang
Training large language models is a computationally intensive process that often requires substantial resources to achieve state-of-the-art results. Incremental layer-wise training…
On the Effectiveness of Interpretable Feedforward Neural Network
Miles Q. Li, Benjamin C. M. Fung, Adel Abusitta
Deep learning models have achieved state-of-the-art performance in many classification tasks. However, most of them cannot provide an interpretation for their classification result…