GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval

Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query. Recent methods leverage Large Language Models (LLMs) to improve query reformulation, but often generate limited and redundant expansions, potentially constraining their effectiveness in capturing diverse intents. In this paper, we propose GenCRF: a Generative Clustering and Reformulation Framework to capture diverse intentions adaptively based on multiple differentiated, well-generated queries in the retrieval phase for the first time. GenCRF leverages LLMs to generate variable queries from the initial query using customized prompts, then clusters them into groups to distinctly represent diverse intents. Furthermore, the framework explores to combine diverse intents query with innovative weighted aggregation strategies to optimize retrieval performance and crucially integrates a novel Query Evaluation Rewarding Model (QERM) to refine the process through feedback loops. Empirical experiments on the BEIR benchmark demonstrate that GenCRF achieves state-of-the-art performance, surpassing previous query reformulation SOTAs by up to 12% on nDCG@10. These techniques can be adapted to various LLMs, significantly boosting retriever performance and advancing the field of Information Retrieval.

Iterative Matching Point

In this paper, we propose a neural network-based point cloud registration method named Iterative Matching Point (IMP). Our model iteratively matches features of points from two point clouds and solve the rigid body motion by minimizing the distance between the matching points. The idea is similar to Iterative Closest Point (ICP), but our model determines correspondences by comparing geometric features instead of just finding the closest point. Thus it does not suffer from the local minima problem and can handle point clouds with large rotation angles. Furthermore, the robustness of the feature extraction network allows IMP to register partial and noisy point clouds. Experiments on the ModelNet40 dataset show that our method outperforms existing point cloud registration method by a large margin, especially when the initial rotation angle is large. Also, its capability generalizes to real world 2.5D data without training on them.