Fine-tuning LLMs with Span-Based Human Feedback
Published:Dec 29, 2025 18:51
•1 min read
•ArXiv
Analysis
This paper introduces a novel approach to fine-tuning language models (LLMs) using fine-grained human feedback on text spans. The method focuses on iterative improvement chains where annotators highlight and provide feedback on specific parts of a model's output. This targeted feedback allows for more efficient and effective preference tuning compared to traditional methods. The core contribution lies in the structured, revision-based supervision that enables the model to learn from localized edits, leading to improved performance.
Key Takeaways
- •Proposes a method for fine-tuning LLMs using fine-grained human feedback on text spans.
- •Employs feedback-driven improvement chains where annotators provide targeted feedback.
- •Outperforms direct alignment methods, demonstrating the effectiveness of structured, revision-based supervision.
- •Focuses on localized edits, leading to more efficient preference tuning.
Reference
“The approach outperforms direct alignment methods based on standard A/B preference ranking or full contrastive rewrites, demonstrating that structured, revision-based supervision leads to more efficient and effective preference tuning.”