3 papers
cs.CL2026
Scaling Multi-Hop Training Data via Graph-Constrained Path Selection
Pengyu Chen, Yonggang Zhang, Mingming Chen +3
Endowing large language models with compositional reasoning over specialized documents requires multi-hop training data at scale, where such data rarely exists outside of curated b…
cs.CL2026
Semantic Voting: A Self-Evaluation-Free Approach for Efficient LLM Self-Improvement on Unverifiable Open-ended Tasks
Chunyang Jiang, Yonggang Zhang, Yiyang Cai +5
The rising cost of acquiring supervised data has driven significant interest in self-improvement for large language models (LLMs). Straightforward unsupervised signals like majorit…
cs.CL2025
Efficient Adaptive Rejection Sampling for Accelerating Speculative Decoding in Large Language Models
Chendong Sun, Ali Mao, Lei Xu +1
Speculative Decoding is a prominent technique for accelerating the autoregressive inference of large language models (LLMs) by employing a fast draft model to propose candidate tok…