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cs.CL2026
MAIGO: Mitigating Lost-in-Conversation with History-Cleaned On-Policy Self-Distillation
Haoyu Zheng, Yun Zhu, Shu Yuan +5
Large language models often solve tasks from a fully specified prompt but degrade when the same requirements unfold over multiple turns, known as the lost-in-conversation (LiC) gap…
cs.CL2026
CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging
Jie Cao, Zhenxuan Fan, Zhuonan Wang +8
Large language models (LLMs) achieve remarkable performance on diverse downstream and domain-specific tasks via parameter-efficient fine-tuning (PEFT). However, existing PEFT metho…