9 papers
SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models
Rong Wan, Suliu Qin, Jiaxi Li +5
Audio language models (ALMs) are increasingly used for audio deepfake detection (ADD), yet existing benchmarks assess their verdicts or rationale plausibility without verifying the…
SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision
Rong Wan, Wei Xie, Jiaxi Li +4
Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD methods are built on predefine…
Grounded Checklist Partial Credit for Agent Skill Trajectories
Suliu Qin, Lu Yin, Xilu Wang
Language-model agents increasingly tackle long-horizon tasks in interactive environments, yet their evaluation commonly relies on task-level success rates by reducing an entire exe…
ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity
Jiaxi Li, Lu Yin, Li Shen +5
Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck for widespread adoption. Lo…
Long Chain-of-Thought Compression via Fine-Grained Group Policy Optimization
Xinchen Han, Hossam Afifi, Michel Marot +2
Large Language Models (LLMs) often generate unnecessarily verbose Chain-of-Thought (CoT) reasoning that increases computational costs and latency without proportional performance g…
Search or Accelerate: Confidence-Switched Position Beam Search for Diffusion Language Models
Mingyu Cao, Alvaro H. C. Correia, Christos Louizos +2
Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, repeatedly deciding which positions to commit at each step. Standard decoding follows a g…
LOST: Low-rank and Sparse Pre-training for Large Language Models
Jiaxi Li, Lu Yin, Li Shen +6
While large language models (LLMs) have achieved remarkable performance across a wide range of tasks, their massive scale incurs prohibitive computational and memory costs for pre-…
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
Andy Li, Aiden Durrant, Milan Markovic +5
Pruning of deep neural networks has been an effective technique for reducing model size while preserving most of the performance of dense networks, crucial for deploying models on…
OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework
Jiaxi Li, Lu Yin, Xilu Wang
The integration of Large Language Models (LLMs) into autonomous driving systems offers promising enhancements in environmental understanding and decision-making. However, the subst…