5 papers · 1 filter
SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree Search for Code Generation
Bin Xu, Yiguan Lin, Yinghao Li +1
Large language models demonstrate exceptional performance in simple code generation tasks but still face challenges in tackling complex problems. These challenges may stem from ins…
Extrapolation Merging: Keep Improving With Extrapolation and Merging
Yiguan Lin, Bin Xu, Yinghao Li +1
Large Language Models (LLMs) require instruction fine-tuning to perform different downstream tasks. However, the instruction fine-tuning phase still demands significant computation…
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
Huashan Sun, Yixiao Wu, Yuhao Ye +4
Language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level d…
Word Matters: What Influences Domain Adaptation in Summarization?
Yinghao Li, Siyu Miao, Heyan Huang +1
Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the m…
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment
Heyan Huang, Yinghao Li, Huashan Sun +2
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as…