Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

Retrieval-augmented generation has raise extensive attention as it is promising to address the limitations of large language models including outdated knowledge and hallucinations. However, retrievers struggle to capture relevance, especially for queries with complex information needs. Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. In this paper, we show that strong performance can be achieved by a method we call ITER-RETGEN, which synergizes retrieval and generation in an iterative manner: a model’s response to a task input shows what might be needed to finish the task, and thus can serve as an informative context for retrieving more relevant knowledge which in turn helps generate a better response in another iteration. Compared with recent work which interleaves retrieval with generation when completing a single output, ITER- RETGEN processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.

Introduction. Generative Large Language Models (LLMs) have powered numerous applications, with wellperceived utility. Despite being powerful, LLMs lack knowledge that is under-represented in their training data, and are prone to hallucinations, especially in open-domain settings (OpenAI, 2023). Retrieval-augmented LLMs, therefore, have raised widespread attention as LLM outputs can be potentially grounded on external knowledge. Previous retrieval-augmented LMs (Izacard et al., 2022b; Shi et al., 2023) typically adopted one-time retrieval, i.e., to retrieve knowledge using only the task input (e.g., a user question for open-domain question answering). One-time retrieval should suffice to fulfill the information needs if they are clearly stated in the original input, which is applicable to factoid question answering (Kwiatkowski et al., 2019) and single-hop fact verification (Thorne et al., 2018), but not to tasks with complex information needs, e.g., multi-hop reasoning (Yang et al., 2018) and long-form question answering (Fan et al., 2019).

Discussion / Conclusion. We demonstrate the effectiveness of ITER-RETGEN in answering questions with complex information needs. Despite simple, ITER-RETGEN outperforms retrieval-augmented methods that have a more complex workflow, which we believe could serve as a strong baseline for future research on retrieval-augmented generation. We also show that generation-augmented retrieval adaptation can further improve the performance of ITER-RETGEN while also reducing overheads. In this work, we propose to enhance retrievalaugmented large language models with ITER- RETGEN which synergizes retrieval and generation in an iterative manner, and demonstrates strong performance compared to more structured prompting techniques such as Self-Ask. However, it’s worth noting that our experiments utilized a fixed black-box large language model, which may not have been equally optimized for various forms of prompting. It would be intriguing to investigate the potential of prompting-specific (gradient-based) optimization in pushing the limits further.