5 papers
Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation
Zi Lin, Sheng Shen, Ilia Kulikov +3
Recent advances in large language models (LLMs) have improved their performance on coding benchmarks. However, improvement is plateauing due to the exhaustion of readily available…
Watermarks for Language Models via Probabilistic Automata
Yangkun Wang, Jingbo Shang
A recent watermarking scheme for language models achieves distortion-free embedding and robustness to edit-distance attacks. However, it suffers from limited generation diversity a…
ReMem: Mutual Information-Aware Fine-tuning of Pretrained Vision Transformers for Effective Knowledge Distillation
Chengyu Dong, Huan Gui, Noveen Sachdeva +6
Knowledge distillation from pretrained visual representation models offers an effective approach to improve small, task-specific production models. However, the effectiveness of su…
VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM
Lesheng Jin, Zhenyuan Ruan, Haohui Mai +1
Modern GPUs evolve rapidly, yet production compilers still rely on hand-crafted register allocation heuristics that require substantial re-tuning for each hardware generation. We i…
When is the consistent prediction likely to be a correct prediction?
Alex Nguyen, Dheeraj Mekala, Chengyu Dong +1
Self-consistency (Wang et al., 2023) suggests that the most consistent answer obtained through large language models (LLMs) is more likely to be correct. In this paper, we challeng…