RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation
RIMS introduces a three-stage preference optimization framework specifically designed to enhance small-scale language models (SLMs) in Retrieval-Augmented Generation (RAG) scenarios. The method utilizes synthetic chain-of-thought data generation via rejection sampling with the target SLM, eliminating reliance on proprietary models for data creation. A novel differentiable soft aggregation mechanism replaces hard selection operators, preserving gradient signals from all preference pairs while mai
Analysis
TL;DR
- RIMS introduces a three-stage preference optimization framework specifically designed to enhance small-scale language models (SLMs) in Retrieval-Augmented Generation (RAG) scenarios.
- The method utilizes synthetic chain-of-thought data generation via rejection sampling with the target SLM, eliminating reliance on proprietary models for data creation.
- A novel differentiable soft aggregation mechanism replaces hard selection operators, preserving gradient signals from all preference pairs while maintaining discriminative power.
- Theoretical analysis proves that smoothed approximation offers controllable error bounds and tighter gradient alignment to the oracle objective compared to hard selection methods.
- Empirical results demonstrate consistent improvements in Exact Match and F1 scores across four multi-hop QA benchmarks under noisy retrieval conditions.
Why It Matters
This research addresses a critical bottleneck in deploying efficient AI systems: the vulnerability of small-scale models to noise in retrieval-augmented contexts. By providing a method that improves performance without requiring expensive proprietary models for data generation, RIMS enables more accessible and cost-effective fine-tuning strategies for resource-constrained environments.
Technical Details
- Synthetic Data Generation: Employs rejection sampling using the target SLM itself to generate chain-of-thought preference data, avoiding dependency on larger or proprietary teacher models.
- Smoothed Multi-pair Aggregation: Replaces traditional hard argmin/argmax selection with a differentiable soft operator, allowing gradients to flow from all preference pairs rather than just the single hardest one.
- Theoretical Guarantees: Provides mathematical proofs showing that the smoothed objective has a controllable error bound and achieves better gradient alignment with the true oracle objective than discrete selection methods.
- Benchmark Performance: Evaluated on four multi-hop question answering benchmarks, showing superior performance over state-of-the-art baselines like RoseRAG across various SLM backbones, particularly when retrieved evidence contains noise.
Industry Insight
- Organizations deploying edge devices or low-latency services should consider adopting smoothed aggregation techniques to maximize the utility of smaller, more efficient models.
- The self-supervised nature of the data generation process reduces operational costs and dependency on external API providers, making high-quality preference optimization more scalable for independent developers and smaller enterprises.
- As retrieval systems become more complex, robustness to noisy evidence will be a key differentiator; methods like RIMS offer a pathway to maintain accuracy without increasing model size.
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