Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning

2025-10-22

AI Summary

To address the challenge of efficient and controllable fine-tuning of large language models (LLMs) under semantic conditions such as culture and politics, this paper proposes Zhyper: the first framework to employ factorized hypernetworks for generating context-aware LoRA adapters, dynamically constructing lightweight adaptation modules conditioned on textual descriptions. By integrating text-conditioned embeddings with low-rank adaptation, Zhyper achieves fine-grained value alignment without introducing additional parameters into the backbone model. Experiments demonstrate that Zhyper matches the performance of mainstream methods across multiple benchmarks while reducing trainable parameters by up to 26×. Notably, it significantly improves generalization and instruction-following accuracy in cross-cultural alignment tasks. This work establishes a novel paradigm for semantic-condition-driven, efficient, and controllable LLM fine-tuning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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

Abstract

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning due to the inductive bias of the pre-training and alignment datasets. Prior works have focused on fine-tuning LLMs by directly conditioning the LoRA weights; however, such methods introduce a large number of parameters. As a remedy, we propose Zhyper, a parameter-efficient factorized hypernetwork framework that generates context-aware LoRA adapters from textual descriptions. Experiments on multiple benchmarks show that Zhyper achieves competitive performance with up to 26x fewer parameters than the state-of-the-art baselines. Furthermore, we extend Zhyper to cultural alignment, demonstrating improved generalization to out-of-domain settings and a better capturing of fine-grained contextual values.

Problem

• Conditioning LLMs on specific cultural or semantic values
• Reducing parameter overhead in fine-tuned LoRA adapters
• Improving generalization for cultural alignment tasks

Innovation

• Factorized hypernetwork generates LoRA adapters
• Textual descriptions create context-aware fine-tuning
• Parameter-efficient framework reduces parameters significantly