Papers with hypernetwork-based
Client-Customized Adaptation for Parameter-Efficient Federated Learning (2023.findings-acl)
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| Challenge: | Pre-trained language models have a large memory footprint and are difficult to use in federated learning (FL) |
| Approach: | They propose a hypernetwork-based FL framework that generates client-specific adapters by conditioning the client information. |
| Outcome: | The proposed framework maximizes the utility of shared model parameters while minimizing divergence caused by client heterogeneity. |
Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models (2026.findings-acl)
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| Challenge: | Using a Llama-3.2-3B-Instruct backbone, we evaluate four adaptation mechanisms across four benchmarks: Gorilla APIBench, Spider 2.0, WebArena, and InterCode. |
| Approach: | They compare hypernetwork-based LoRA adaptation against carefully designed few-shot prompting in a controlled experiment . they find that few- shot prompting contributes +21.5% to performance and documentation contributes 0% . |
| Outcome: | The hypernetwork-based LoRA adaptation provides no measurable improvement over few-shot prompting alone. |