# Xetrieval： 机制性地解释密集检索

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-28 08:00
- AIHOT 分数：69
- AIHOT 链接：https://aihot.virxact.com/items/cmpr0qtg109vwslnowj2f968v
- 原文链接：https://arxiv.org/abs/2605.29507

## AI 摘要

Xetrieval是一种用于解释密集检索行为的嵌入级别的机制性框架。它首先引入轻量级推理内化器，通过单次前向传播在嵌入空间近似链式推理，以增强句子嵌入的推理信息。随后，框架将这些推理增强的嵌入分解为稀疏的、人类可解释的特征，并为每个特征赋予自然语言描述。通过聚合多个文档侧视图的稀疏特征重叠，Xetrieval能够为单个检索决策提供特征级别的解释。实验表明，该方法在不同检索器和基准上能发现连贯的可解释特征，并支持任务级的特征引导。

## 正文

Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high-dimensional embeddings. Existing explanations often focus on surface signals, such as lexical matches, token alignments, or post-hoc textual rationales, and thus provide limited insight into the latent factors that shape dense retrieval behavior at the embedding level. We propose Xetrieval, an embedding-level mechanistic framework for explaining dense retrieval. Xetrieval first introduces a lightweight reasoning internalizer that approximates Chain-of-Thought reasoning directly in the embedding space with a single forward pass, enriching sentence embeddings with reasoning-oriented information while avoiding expensive autoregressive generation. It then decomposes these reasoning-enhanced embeddings into sparse, human-interpretable features, each associated with a coherent natural language description. By aggregating sparse feature overlaps across multiple document-side views, Xetrieval provides feature-level explanations of individual retrieval decisions. Experiments on diverse retrievers and benchmarks show that Xetrieval uncovers coherent interpretable features, yields stronger pair-level intervention effects, and supports task-level feature steering. The project page and source code are available at https://hihiczx.github.io/Xetrieval .