LLM-guided Hierarchical Retrieval

• URL: http://arxiv.org/abs/2510.13217v1
• Date: Wed, 15 Oct 2025 07:05:17 GMT
• Title: LLM-guided Hierarchical Retrieval
• Abstract summary: LATTICE is a hierarchical retrieval framework that enables an LLM to reason over and navigate large corpora with logarithmic search complexity.<n>A central challenge in such LLM-guided search is that the model's relevance judgments are noisy, context-dependent, and unaware of the hierarchy.<n>Our framework achieves state-of-the-art zero-shot performance on the reasoning-intensive BRIGHT benchmark.
• Score: 54.73080745446999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern IR systems are increasingly tasked with answering complex, multi-faceted queries that require deep reasoning rather than simple keyword or semantic matching. While LLM-based IR has shown great promise, the prevailing retrieve-then-rerank paradigm inherits the limitations of embedding-based retrieval; parametric generative approaches are difficult to update with new information; and long-context methods that place the entire corpus in context are computationally infeasible for large document collections. To address these challenges, we introduce LATTICE, a hierarchical retrieval framework that enables an LLM to reason over and navigate large corpora with logarithmic search complexity by imposing a semantic tree structure on the corpus. Our approach consists of two stages: (1) an offline phase that organizes the corpus into a semantic hierarchy via either a bottom-up agglomerative strategy or a top-down divisive strategy using multi-level summaries and (2) an online traversal phase where a search LLM navigates this tree. A central challenge in such LLM-guided search is that the model's relevance judgments are noisy, context-dependent, and unaware of the hierarchy, making cross-branch and cross-level comparisons difficult. To overcome this, we propose a traversal algorithm that estimates calibrated latent relevance scores from local LLM outputs and aggregates them into a global path relevance metric. Our training-free framework achieves state-of-the-art zero-shot performance on the reasoning-intensive BRIGHT benchmark, demonstrating up to 9% improvement in Recall@100 and 5% in nDCG@10 over the next best zero-shot baseline. Furthermore, compared to the fine-tuned SOTA method DIVER-v2, LATTICE attains comparable results on BRIGHT subsets that use a static corpus for evaluation.

Related papers

• SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG [23.48941508046885]
  We present SproutRAG, an attention-guided hierarchical RAG framework.<n>It organizes sentence-level chunks into progressively larger but semantically coherent units.<n>It learns which attention heads and layers best capture semantic document structure.
  arXiv  Detail & Related papers  (2026-06-16T18:28:00Z)
• HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy [54.91468501159335]
  Cross-document relation extraction (RE) aims to identify relations between the head tail entities located in different documents.<n>We propose an inference strategy that improves reliability through multi-view verification at each level.
  arXiv  Detail & Related papers  (2026-04-09T07:55:27Z)
• SCOPE: Tree-based Self-Correcting Online Log Parsing via Syntactic-Semantic Collaboration [3.338964978828878]
  SCOPE is the first self-correcting online log parsing method.<n>It integrates the strengths of both and LLM-based paradigms.<n>It outperforms state-of-the-art methods in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-03-28T11:45:08Z)
• FABLE: Forest-Based Adaptive Bi-Path LLM-Enhanced Retrieval for Multi-Document Reasoning [2.9863062339019817]
  We present textbfFABLE, a textbfForest-based textbfAdaptive textbfBi-path textbfLLM-textbfEnhanced retrieval framework.
  arXiv  Detail & Related papers  (2026-01-26T04:00:56Z)
• Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition [20.966359103135762]
  We show that LLMs can uncover latent intent behind superlatives in e-commerce queries.<n>Our approach decomposes queries into attribute-value hints generated concurrently with retrieval.<n>Our method improves search performanc eby 10.9 points in MAP and ranking by 5.9 points in MRR over baselines.
  arXiv  Detail & Related papers  (2025-11-17T23:53:25Z)
• LLMs as Sparse Retrievers:A Framework for First-Stage Product Search [103.70006474544364]
  Product search is a crucial component of modern e-commerce platforms, with billions of user queries every day.<n>Sparse retrieval methods suffer from severe vocabulary mismatch issues, leading to suboptimal performance in product search scenarios.<n>With their potential for semantic analysis, large language models (LLMs) offer a promising avenue for mitigating vocabulary mismatch issues.<n>We propose PROSPER, a framework for PROduct search leveraging LLMs as SParsE Retrievers.
  arXiv  Detail & Related papers  (2025-10-21T11:13:21Z)
• Automated Skill Decomposition Meets Expert Ontologies: Bridging the Granularity Gap with LLMs [1.2891210250935148]
  This paper investigates automated skill decomposition using Large Language Models (LLMs)<n>Our framework standardizes the pipeline from prompting and generation to normalization and alignment with ontology nodes.<n>To evaluate outputs, we introduce two metrics: a F1-score that uses optimal embedding-based matching to assess content accuracy, and a hierarchy-aware F1-score that credits structurally correct placements to assess granularity.
  arXiv  Detail & Related papers  (2025-10-13T12:03:06Z)
• CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning [67.18702329644526]
  CoT Referring enhances model reasoning across modalities through a structured, chain-of-thought training data structure.<n>We restructure the training data to enforce a new output form, providing new annotations for existing datasets.<n>We also integrate detection and segmentation capabilities into a unified MLLM framework, training it with a novel adaptive weighted loss to optimize performance.
  arXiv  Detail & Related papers  (2025-10-03T08:50:21Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation [35.70731674603417]
  Large Language Models (LLMs) can be used to augment both user queries and corpus documents.<n>We present an LLM-based retriever empowered to augment both user queries and corpus documents.<n>Our approach significantly enhances LLM-based retrieval performance in both sparse and dense settings.
  arXiv  Detail & Related papers  (2025-06-23T14:14:43Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• ELITE: Embedding-Less retrieval with Iterative Text Exploration [5.8851517822935335]
  Large Language Models (LLMs) have achieved impressive progress in natural language processing.<n>Their limited ability to retain long-term context constrains performance on document-level or multi-turn tasks.
  arXiv  Detail & Related papers  (2025-05-17T08:48:43Z)
• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• HIRO: Hierarchical Information Retrieval Optimization [0.0]
  Retrieval-Augmented Generation (RAG) has revolutionized natural language processing by dynamically integrating external knowledge into Large Language Models (LLMs) Recent implementations of RAG leverage hierarchical data structures, which organize documents at various levels of summarization and information density. This complexity can cause LLMs to "choke" on information overload, necessitating more sophisticated querying mechanisms.
  arXiv  Detail & Related papers  (2024-06-14T12:41:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.