Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method (2501.18539v1)

Abstract: Real-world open-domain questions can be complicated, particularly when answering them involves information from multiple information sources. LLMs have demonstrated impressive performance in decomposing complex tasks into simpler steps, and previous work has used it for better retrieval in support of complex questions. However, LLM's decomposition of questions is unaware of what data is available and how data is organized, often leading to a sub-optimal retrieval performance. Recent effort in agentic RAG proposes to perform retrieval in an iterative fashion, where a followup query is derived as an action based on previous rounds of retrieval. While this provides one way of interacting with the data collection, agentic RAG's exploration of data is inefficient because successive queries depend on previous results rather than being guided by the organization of available data in the collection. To address this problem, we propose an LLM-based retrieval method -- ARM, that aims to better align the question with the organization of the data collection by exploring relationships among data objects beyond matching the utterance of the query, thus leading to a retrieve-all-at-once solution for complex queries. We evaluated ARM on two datasets, Bird and OTT-QA. On Bird, it outperforms standard RAG with query decomposition by up to 5.2 pt in execution accuracy and agentic RAG (ReAct) by up to 15.9 pt. On OTT-QA, it achieves up to 5.5 pt and 19.3 pt higher F1 match scores compared to these approaches.

An Essay on the Paper "Can we Retrieve Everything All at Once? \ARM: An \underline{A}lignment-Oriented LLM-based \underline{R}etrieval \underline{M}ethod"

The concept of enhancing retrieval mechanisms with alignment-oriented strategies concerning LLMs has been the focal point of the paper titled "Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method." The authors address a significant challenge in the field of information retrieval and open-domain question answering—optimizing the retrieval process for complex queries that span multiple data sources, often requiring a nuanced understanding not just of the query itself but of how the data is organized.

Problem Context

The complexity of answering real-world open-domain questions lies in the necessity to distill information from various sources such as text corpora, databases, and other modalities like images. Traditional approaches, notably Retrieval-Augmented Generation (RAG), often utilize LLMs for query decomposition without full alignment with available data. Issue arises when LLMs, albeit capable of breaking down complex questions into simpler steps, remain unaware of how data is organized, thereby leading to retrieval inefficiencies.

Key Contributions and Results

The paper introduces \ARM (Alignment-Oriented LLM-based Retrieval Method), a novel integrative approach that aims to improve retrieval efficiency and accuracy by aligning question decomposition with the organization of data collections. The method emphasizes reasoning about the relationships among data objects beyond straightforward query matching. ARM differentiates itself from agentic RAG approaches, which iteratively enhance queries based on prior results yet potentially suffer from inefficiencies due to successive, unaligned queries.

1. Evaluation and Performance: ARM was evaluated using two datasets, Bird and OTT-QA. For Bird, ARM outperformed standard RAG with query decomposition by up to 5.2 points in execution accuracy and ReAct, an agentic RAG variant, by up to 15.9 points. On the OTT-QA dataset, ARM delivered improved F1 match scores by up to 5.5 points compared to query decomposition RAG and 19.3 points over ReAct, highlighting the method's robustness and enhanced retrieval efficacy.
2. Retrieval Efficiency: ARM's methodology to "retrieve-everything-all-at-once" notably reduces LLM calls compared to iterative approaches, resulting in less inference time and cost. This efficiency is achieved by employing a comprehensive semantic alignment process tailored to complex queries.

Methodological Advances

ARM's retrieval mechanism hinges on advanced alignment processes, incorporating:

• Information Alignment: It aligns questions with N-gram data indexed from existing data objects, empowering constrained beam decoding to find relationships beyond mere query terms.
• Structure Alignment: Employs a solver to optimize and ensure that selected data objects collectively address the components of the query. Utilizing a mixed-integer programming framework, it maximizes compatibility and relevance scores across data objects.
• Self-Verification: LLMs themselves are utilized to verify the comprehensiveness and correctness of the aggregated data, effectively diminishing potential reasoning derailments common in previous agentic approaches.

Implications and Future Scope

The practical implications of ARM's contributions are considerable, particularly in domains requiring precise and multi-faceted data retrieval such as enterprise data management, large-scale question-answering systems, and domain-specific databases. Theoretically, this paper underscores the importance of data-specific alignment in question decomposition and retrieval processes.

Looking forward, the evolution of LLM capabilities, particularly self-verification of aligned data, might pave the way for even more autonomous and efficient retrieval systems. Moreover, integrating multimodal data sources into ARM may expand its applicability and efficacy across varying domains in artificial intelligence.

In summary, this paper introduces a significant methodological advance in the integration of LLMs with retrieval processes, providing both a practical solution to existing inefficiencies and contributing valuable insights into the theoretical understanding of data alignment in complex query situations.