Showing cs.CLShow all
2 papers · 1 filter
cs.CL2025
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
Ming Li, Pei Chen, Chenguang Wang +5
Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primar…
cs.CL2025
RuleR: Improving LLM Controllability by Rule-based Data Recycling
Ming Li, Han Chen, Chenguang Wang +3
Large language models (LLMs) still lack delicate controllability over their responses, which is critical to enhancing their performance and the user experience. However, curating s…