# Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models

arXiv: https://arxiv.org/abs/2506.00726 · HTML: https://mlsanity.com/paper/2506.00726/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 12 of 12)

- [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://mlsanity.com/paper/2403.03507/) (2024)
- [Fine-Tuning Language Models with Just Forward Passes](https://mlsanity.com/paper/2305.17333/) (2023)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [Learning from models beyond fine-tuning](https://mlsanity.com/paper/2310.08184/) (2023)
- [Gradient-based Parameter Selection for Efficient Fine-Tuning](https://mlsanity.com/paper/2312.10136/) (2023)
- [Trainable Projected Gradient Method for Robust Fine-tuning](https://mlsanity.com/paper/2303.10720/) (2023)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [Comparison of fine-tuning strategies for transfer learning in medical image classification](https://mlsanity.com/paper/2406.10050/) (2024)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination](https://mlsanity.com/paper/2504.15577/) (2025)
- [ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks](https://mlsanity.com/paper/2407.21066/) (2024)
- [DeepSORT-Driven Visual Tracking Approach for Gesture Recognition in Interactive Systems](https://mlsanity.com/paper/2505.07110/) (2025)

## Built on by (most cited first, 3 of 3)

- [Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction](https://mlsanity.com/paper/2507.01437/) (2025)
- [Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment](https://mlsanity.com/paper/2507.15198/) (2025)
- [Semantic and Structural Analysis of Implicit Biases in Large Language Models: An Interpretable Approach](https://mlsanity.com/paper/2508.06155/) (2025)

## Abstract (arXiv)

This paper presents a gradient-informed fine-tuning method for large language models under few-shot conditions. The goal is to enhance task adaptability and training stability when data is limited. The method builds on a base loss function and introduces two gradient-related regularization terms. The first enforces gradient direction consistency to guide parameter updates along task-relevant directions and prevent drift. The second controls gradient magnitude to avoid abnormal updates. Together, these components support a more efficient and stable optimization path. To further improve cross-task generalization, the method incorporates a gradient alignment mechanism. This mechanism measures the consistency between optimization directions of the source and target tasks. It enhances fine-tuning performance in multi-task and cross-domain scenarios. Across various natural language understanding tasks, the method outperforms existing fine-tuning strategies in average accuracy, gradient stability, and directional alignment. Empirical evaluations under different sample sizes and domain-specific tasks confirm the method's robustness and broad applicability in low-resource environments. In particular, the method shows clear advantages in controlling parameter update paths. The results demonstrate that a gradient-based fine-tuning framework can effectively leverage the representational power of large language models. It ensures training stability while reducing dependence on large volumes of labeled data.