6 citations · 6 across the 10 of their papers we have counts for
10 papers
Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks
Yi Yang, Xiaoke Chen, Jinyang Huang +6
Backdoor attacks compromise training data so that a model retains clean accuracy but predicts an attacker-chosen target on triggered inputs. At very low poisoning rates, only a few…
AcademiClaw: When Students Set Challenges for AI Agents
Junjie Yu, Pengrui Lu, Weiye Si +75
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We intro…
Checkerboard: Closed-Form and Data-Independent Trigger Design for Clean-Label Backdoor Attacks
Yi Yang, Jinyang Huang, Binbin Liu +6
Backdoor attacks threaten the deep-learning supply chain by poisoning a small fraction of the training data so that a model behaves normally on clean inputs but maps triggered inpu…
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
Siqing Song, Chuang Wang, Yong Lang +2
Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBLLM, a lightweight binarization…
TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs
Jing Peng, Chenghao Wang, Yi Yang +5
Speech LLM post-training increasingly relies on efficient cross-modal alignment and robust low-resource adaptation, yet collecting large-scale audio-text pairs remains costly. Text…
Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference
Quantong Qiu, Zhiyi Hong, Yi Yang +5
The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. While hybrid attention mechanis…
Sparse Rewards Can Self-Train Dialogue Agents
Barrett Martin Lattimer, Varun Gangal, Ryan McDonald +1
Recent advancements in state-of-the-art (SOTA) Large Language Model (LLM) agents, especially in multi-turn dialogue tasks, have been primarily driven by supervised fine-tuning and…
Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses
Dongxu Zhang, Varun Gangal, Barrett Martin Lattimer +1
Detecting hallucinations in large language model (LLM) outputs is pivotal, yet traditional fine-tuning for this classification task is impeded by the expensive and quickly outdated…
DYAD: A Descriptive Yet Abjuring Density efficient approximation to linear neural network layers
Sarin Chandy, Varun Gangal, Yi Yang +1
We devise, implement and performance-asses DYAD, a layer which can serve as a faster and more memory-efficient approximate replacement for linear layers, (nn.Linear() in Pytorch).…
Fast and Accurate Factual Inconsistency Detection Over Long Documents
Barrett Martin Lattimer, Patrick Chen, Xinyuan Zhang +1
Generative AI models exhibit remarkable potential; however, hallucinations across various tasks present a significant challenge, particularly for longer inputs that current approac…