"Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method"
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https://arxiv.org/abs/2501.18539
The challenge in answering complex real-world questions lies in efficiently retrieving information spread across various sources. Current Large Language Model (LLM) methods decompose questions without considering data availability, leading to ineffective retrieval. Agentic Retrieval-Augmented Generation (RAG) iteratively retrieves but is inefficient due to its sequential nature and lack of awareness of data organization.
This paper introduces Alignment-Oriented LLM-based Retrieval Method (ARM). ARM aligns questions with data organization. It efficiently retrieves all relevant information at once for complex queries.
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📌 ARM innovatively uses constrained decoding for information alignment. It guides the LLM to rephrase keywords using corpus N-grams. This aligns the LLM's query with the data collection vocabulary, enhancing retrieval.
📌 Structure alignment via Mixed-Integer Programming (MIP) is a key contribution. MIP optimizes object selection by considering both relevance and inter-object compatibility. This ensures retrieval of connected, answer-centric object sets.
📌 Self-verification integrates LLM's reasoning with solver-generated drafts. This process lets the LLM evaluate and refine retrieval results. Aggregation via beam search confidence voting further enhances the robustness of object selection.
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Methods Explored in this Paper 🔧:
→ The paper introduces ARM, an Alignment-Oriented Retrieval Method. ARM enhances LLM-based retrieval for complex questions.
→ ARM is designed to align questions with the organization of available data. It explores relationships between data objects beyond simple query matching.
→ ARM consists of three key stages: information alignment, structure alignment, and self-verification and aggregation.
→ Information alignment decomposes the user question into keywords. It then aligns these keywords with N-grams from the data collection using constrained decoding.
→ Structure alignment uses a Mixed-Integer Programming (MIP) solver. This solver identifies connections between data objects to answer the question, considering domain-specific knowledge.
→ Self-verification and aggregation involve the LLM verifying the relevance and connections of retrieved objects. It aggregates results from different alignment drafts to select the final set of objects.
→ Constrained beam decoding and MIP solver are used to guide the LLM in generating a retrieval process aligned with data organization.
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Key Insights 💡:
→ Aligning question decomposition with data organization improves retrieval effectiveness.
→ ARM achieves "retrieve-everything-all-at-once" for complex queries, unlike iterative agentic RAG.
→ ARM reduces the number of LLM calls compared to agentic RAG, improving efficiency.
→ By considering data object relationships, ARM retrieves more relevant information and reduces noise compared to standard RAG and agentic RAG.
→ ARM's approach addresses the inefficiency and potential reasoning derailment issues of agentic RAG.
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Results 📊:
→ On the Bird dataset, ARM outperforms query decomposition RAG by up to 5.2 points in execution accuracy. ARM surpasses ReAct by up to 15.9 points in execution accuracy.
→ On OTT-QA, ARM achieves up to 5.5 points higher F1 match scores than query decomposition RAG. ARM achieves up to 19.3 points higher F1 match scores compared to ReAct.
→ ARM achieves a recall of 96.5 and perfect recall of 92.7 on Bird, retrieving on average 5.00 objects.
→ ARM achieves a recall of 79.8 and perfect recall of 62.5 on OTT-QA, retrieving on average 4.98 objects.
→ ReAct, in comparison, requires 5.26 LLM calls on Bird and 4.52 calls on OTT-QA, while ARM uses only 1 call.