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To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called “CoaCor”), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.
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This content will become publicly available on December 5, 2025
Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events
ABSTRACT Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval‐reranking framework leveraging large language models to enhance the spatiotemporal and semantic associated mining and recommendation of relevant, unusual climate and environmental events described in news articles and web posts. This framework uses advanced natural language processing techniques to address the limitations of traditional manual curation methods in terms of high labor costs and lack of scalability. Specifically, we explore an optimized solution to employ cutting‐edge embedding models for semantically analyzing spatiotemporal events (news) and propose a Geo‐Time Re‐ranking strategy that integrates multi‐faceted criteria including spatial proximity, temporal association, semantic similarity, and category‐instructed similarity to rank and identify similar spatiotemporal events. We apply the proposed framework to a dataset of four thousand local environmental observer network events, achieving top performance on recommending similar events among multiple cutting‐edge dense retrieval models. The search and recommendation pipeline can be applied to a wide range of similar data search tasks dealing with geospatial and temporal data. We hope that by linking relevant events, we can better aid the general public to gain enhanced understanding on climate change and its impact on different communities.
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- Award ID(s):
- 1853864
- PAR ID:
- 10558769
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Transactions in GIS
- Volume:
- 29
- Issue:
- 1
- ISSN:
- 1361-1682
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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