18 papers
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
Zhiwen Ruan, Yichao Du, Jianjie Zheng +6
A promising paradigm for adapting instruction-tuned language models is to learn task-specific updates on a pretrained base model and subsequently merge them into the instruction-tu…
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios
Yutao Hou, Yihan Jiang, Yuhan Xie +5
Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical beha…
Representation-Guided Parameter-Efficient LLM Unlearning
Zeguan Xiao, Lang Mo, Yun Chen +4
Large Language Models (LLMs) often memorize sensitive or harmful information, necessitating effective machine unlearning techniques. While existing parameter-efficient unlearning m…
Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
Zeguan Xiao, Siqing Li, Yong Wang +4
Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM unlearning as an asymmetric…
SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks
Tianyi Wang, Yixia Li, Long Li +6
Proximal Policy Optimization (PPO) is central to aligning Large Language Models (LLMs) in reasoning tasks with verifiable rewards. However, standard token-level PPO struggles in th…
InstructDiff: Domain-Adaptive Data Selection via Differential Entropy for Efficient LLM Fine-Tuning
Junyou Su, He Zhu, Xiao Luo +6
Supervised fine-tuning (SFT) is fundamental to adapting large language models, yet training on complete datasets incurs prohibitive costs with diminishing returns. Existing data se…