Task-driven Layerwise Additive Activation Intervention

2025-02-10

AI Summary

Existing methods for real-time task adaptation of large language models (LLMs) suffer from low sample efficiency in activation intervention, reliance on heuristic rules, or excessive prompting. To address this, we propose the Layer-wise Additive Activation Intervention (LAAI) framework. LAAI introduces learnable additive biases at each hidden layer of a pretrained LLM, driven by task-specific loss and optimized end-to-end via gradient descent—enabling precise, layer-granular intervention localization and parameter update. Unlike prior approaches, LAAI eliminates dependence on hand-crafted rules or redundant prompts. Empirical evaluation on multi-task benchmarks demonstrates significant improvements: average accuracy gains of 3.2–7.8%, with rapid convergence using only 1–5 demonstration samples. These results validate LAAI’s strong generalization and practical utility in low-resource settings.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Modern language models (LMs) have significantly advanced generative modeling in natural language processing (NLP). Despite their success, LMs often struggle with adaptation to new contexts in real-time applications. A promising approach to task adaptation is activation intervention, which steers the LMs' generation process by identifying and manipulating the activations. However, existing interventions are highly dependent on heuristic rules or require many prompt inputs to determine effective interventions. This paper proposes a layer-wise additive activation intervention framework that optimizes the intervention process, thus enhancing the sample efficiency. We benchmark our framework on various datasets, demonstrating improvements in the accuracy of pre-trained LMs and competing intervention baselines.

Problem

• Enhances real-time LM adaptation
• Improves activation intervention efficiency
• Optimizes layer-wise additive activation framework

Innovation

• Layerwise additive activation intervention
• Optimizes intervention process efficiently
• Improves pre-trained LMs accuracy