5 papers · 2 filters
G2: Guided Generation for Enhanced Output Diversity in LLMs
Zhiwen Ruan, Yixia Li, Yefeng Liu +5
Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in outp…
Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning
Zhiwen Ruan, Yixia Li, He Zhu +4
Large language models (LLMs) primarily rely on supervised fine-tuning (SFT) as a key method to adapt pre-trained models to domain-specific tasks such as mathematical reasoning. How…
Unveiling Over-Memorization in Finetuning LLMs for Reasoning Tasks
Zhiwen Ruan, Yun Chen, Yutao Hou +3
The pretrained large language models (LLMs) are finetuned with labeled data for better instruction following ability and alignment with human values. In this paper, we study the le…
TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
He Zhu, Zhiwen Ruan, Junyou Su +4
High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present TAG…
LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy
Zhiwen Ruan, Yixia Li, He Zhu +5
Despite being pretrained on multilingual corpora, large language models (LLMs) exhibit suboptimal performance on low-resource languages. Recent approaches have leveraged multilingu…