Retrieval-Augmented Black-Box LLMs with REP LUG
Introduction
The paper introduces a novel retrieval-augmented LLMing framework, REP LUG, designed to enhance the capabilities of existing LLMs without the necessity to access or modify their internal parameters. This approach is particularly crucial in light of the growing trend where state-of-the-art LLMs are available only as API services, making traditional methods of model enhancement, such as fine-tuning, infeasible. REP LUG contributes to the body of research by demonstrating a flexible, plug-and-play method for incorporating external knowledge into LLMs to improve their performance across several tasks.
Methodology
Retrieval-Augmented LLMing
REP LUG fundamentally shifts from the conventional model of directly training LLMs with retrieved documents through mechanisms like cross-attention. Instead, it retrieves relevant documents based on the input context and prepends these documents to the input of a "frozen" black-box LLM. This method bypasses the need for modifying the LLM itself, enabling enhancement of off-the-shelf models. The framework is compatible with any combination of retrieval models and LLMs, providing significant flexibility and ease of integration.
LM-Supervised Retrieval (LSR)
A further innovation is the introduction of a training scheme for the retrieval model within the framework, termed LM-Supervised Retrieval (LSR). This approach employs the LLM itself to guide the training of the retrieval model, optimizing for documents that, when prepended to the input, result in lower LLM perplexity. Essentially, the LSR aims to align the retriever’s outputs with the LLM's preference for generating accurate predictions, establishing a synergistic relationship between the retriever and the LLM.
Empirical Evaluation
The experiments conducted assess REP LUG's performance in enhancing black-box LLMs across various tasks, including LLMing and multiple-choice question answering (e.g., MMLU). Notably, significant improvements were observed when applying REP LUG to augment GPT-3 and Codex models, with up to a 6.3% performance boost on LLMing tasks. These results underscore the potential of retrieval augmentation to enrich LLMs with external knowledge effectively, thus mitigating the models' innate limitations in knowledge coverage.
Theoretical Implications and Future Directions
The outcomes of this research have profound implications for the development and application of LLMs. By decoupling the enhancement process from the need for internal model access, REP LUG offers a scalable and adaptable solution for improving LLMs post-deployment. Furthermore, the LSR scheme opens avenues for research into more granular optimization of the retrieval process based explicitly on LLM feedback, potentially leading to the creation of more sophisticated and contextually aware retrieval mechanisms. Future exploration may also delve into the interpretability of the retrieval-augmented predictions and the integration of more dynamic retrieval sources beyond static document corpora.
Conclusion
The REP LUG framework presents an innovative methodology for the enhancement of LLMs through retrieval augmentation. Its principal contributions lie in its flexible architecture that accommodates black-box LLMs and the LSR training scheme that fine-tunes the retrieval based on LLM supervision. The reported improvements across diverse modeling tasks attest to the efficacy of REP LUG and advocate for its broader adoption as a tool for expanding the capabilities of existing LLMs. This work paves the way for further research into more efficient, effective, and modular approaches to leveraging external knowledge in augmenting the intelligence of LLMs.