490 citations · 878 across the 55 of their papers we have counts for
19 papers · 1 filter
QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards
Rongzhi Zhang, Rui Feng, Zhihan Zhang +8
Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as…
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning
Haoyu Wang, Tianci Liu, Ruirui Li +3
Pre-trained language models, trained on large-scale corpora, demonstrate strong generalizability across various NLP tasks. Fine-tuning these models for specific tasks typically inv…
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering
Haoyu Wang, Ruirui Li, Haoming Jiang +7
Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challen…
Data Diversity Matters for Robust Instruction Tuning
Alexander Bukharin, Shiyang Li, Zhengyang Wang +6
Recent works have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. However, creating su…
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Qingru Zhang, Chandan Singh, Liyuan Liu +4
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the rea…
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
Qingru Zhang, Dhananjay Ram, Cole Hawkins +2
Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture lo…