Optimizing Extractive LLM Pipelines
PlanE (Meta Planning of Data, Tuning, and Inference) addresses the inefficiencies inherent in traditional extractive LLM workflows. Rather than treating data preparation, fine-tuning, and inference as isolated stages, PlanE introduces a meta-planning layer that dynamically adjusts these components to maximize performance. The core argument is that extractive models—which rely on retrieving and presenting specific information—suffer from performance degradation when these stages are not aligned with the specific constraints of the target dataset or query distribution.
The Meta-Planning Framework
The framework functions as a controller that manages three critical dimensions:
- Data Selection: Instead of uniform training, the system identifies high-utility samples that contribute most to the model's extractive capabilities, reducing noise and training overhead.
- Tuning Strategy: The meta-planner selects optimal fine-tuning parameters based on the specific structure of the extractive task, ensuring the model remains grounded in the source material while minimizing hallucination.
- Inference Optimization: During deployment, the planner adjusts retrieval parameters and decoding strategies in real-time, allowing the model to balance precision and recall based on the complexity of the incoming query.
By integrating these stages, PlanE allows developers to treat the entire pipeline as a single, tunable system. This approach significantly reduces the manual trial-and-error process typically required to align retrieval-augmented generation (RAG) systems with specific domain knowledge, resulting in more robust and accurate extractive outputs.