Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
This work addresses a practical bottleneck for clients in federated learning who lack structured data, broadening the application scope of federated instruction tuning, though it is incremental in automating data transformation.
The paper tackles the problem of federated instruction tuning for large language models by proposing FedIT-U2S, a framework that automatically transforms unstructured text into structured instruction-response pairs, enabling collaborative fine-tuning without requiring pre-annotated data. Experiments across medicine, knowledge, and math domains show that FedIT-U2S consistently and significantly improves the base LLM, with concrete gains in performance metrics.
Federated instruction tuning enables multiple clients to collaboratively fine-tune a shared large language model (LLM) that can follow humans' instructions without directly sharing raw data. However, existing literature impractically requires that all the clients readily hold instruction-tuning data (i.e., structured instruction-response pairs), which necessitates massive human annotations since clients' data is usually unstructured text instead. Addressing this, we propose a novel and flexible framework FedIT-U2S, which can automatically transform unstructured corpus into structured data for federated instruction tuning. FedIT-U2S consists two key steps: (1) few-shot instruction-tuning data generation, where each unstructured data piece together with several examples is combined to prompt an LLM in generating an instruction-response pair. To further enhance the flexibility, a retrieval-based example selection technique is proposed, where the examples are automatically selected based on the relatedness between the client's data piece and example pool, bypassing the need of determining examples in advance. (2) A typical federated instruction tuning process based on the generated data. Overall, FedIT-U2S can be applied to diverse scenarios as long as the client holds valuable text corpus, broadening the application scope of federated instruction tuning. We conduct a series of experiments on three domains (medicine, knowledge, and math), showing that our proposed FedIT-U2S can consistently and significantly brings improvement over the base LLM.