3 citations · 5 across the 8 of their papers we have counts for
8 papers
OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction
Skyler Hallinan, Thejas Venkatesh, Xiang Ren +4
Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks. While most existing benchmarks assume simple, perfectly documented tools, real-world t…
Competitive Non-Clairvoyant KV-Cache Scheduling for LLM Inference
Yiding Feng, Zonghan Yang, Yuhao Zhang
Large Language Model (LLM) inference presents a unique scheduling challenge due to the Key-Value (KV) cache, where a job's memory footprint grows linearly with the number of decode…
Robust Uncertainty Quantification for Factual Generation of Large Language Models
Yuhao Zhang, Zhongliang Yang, Linna Zhou
The rapid advancement of large language model(LLM) technology has facilitated its integration into various domains of professional and daily life. However, the persistent challenge…
A One-Layer Decoder-Only Transformer is a Two-Layer RNN: With an Application to Certified Robustness
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
This paper reveals a key insight that a one-layer decoder-only Transformer is equivalent to a two-layer Recurrent Neural Network (RNN). Building on this insight, we propose ARC-Tra…
CodeFort: Robust Training for Code Generation Models
Yuhao Zhang, Shiqi Wang, Haifeng Qian +8
Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models. Although improvin…
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models
Yi Luo, Zhenghao Lin, Yuhao Zhang +7
Large Language Models (LLMs) exhibit impressive capabilities but also present risks such as biased content generation and privacy issues. One of the current alignment techniques in…
Verified Training for Counterfactual Explanation Robustness under Data Shift
Anna P. Meyer, Yuhao Zhang, Aws Albarghouthi +1
Counterfactual explanations (CEs) enhance the interpretability of machine learning models by describing what changes to an input are necessary to change its prediction to a desired…
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System
Haoyuan Li, Hao Jiang, Tianke Zhang +6
Training AI models has always been challenging, especially when there is a need for custom models to provide personalized services. Algorithm engineers often face a lengthy process…