most citedLoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model

4 citations · 7 across the 10 of their papers we have counts for

collaborators

21 papers

cs.CL20241 cited

AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition

Zhaorun Chen, Zhuokai Zhao, Zhihong Zhu +4

Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback…

cs.CL2024

Evaluating and Improving Continual Learning in Spoken Language Understanding

Muqiao Yang, Xiang Li, Umberto Cappellazzo +2

Continual learning has emerged as an increasingly important challenge across various tasks, including Spoken Language Understanding (SLU). In SLU, its objective is to effectively h…

cs.RO20241 cited

Customizable Perturbation Synthesis for Robust SLAM Benchmarking

Xiaohao Xu, Tianyi Zhang, Sibo Wang +6

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Sim…

cs.SD2024

Domain Adaptation for Contrastive Audio-Language Models

Soham Deshmukh, Rita Singh, Bhiksha Raj

Audio-Language Models (ALM) aim to be general-purpose audio models by providing zero-shot capabilities at test time. The zero-shot performance of ALM improves by using suitable tex…

cs.LG2024

A General Framework for Learning from Weak Supervision

Hao Chen, Jindong Wang, Lei Feng +6

Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algor…

cs.LG2024

On Catastrophic Inheritance of Large Foundation Models

Hao Chen, Bhiksha Raj, Xing Xie +1

Large foundation models (LFMs) are claiming incredible performances. Yet great concerns have been raised about their mythic and uninterpreted potentials not only in machine learnin…