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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.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…
cs.CL2025
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…