@inproceedings{dhole-etal-2024-queryexplorer,
title = "{Q}uery{E}xplorer: An Interactive Query Generation Assistant for Search and Exploration",
author = "Dhole, Kaustubh and
Bajaj, Shivam and
Chandradevan, Ramraj and
Agichtein, Eugene",
editor = "Chang, Kai-Wei and
Lee, Annie and
Rajani, Nazneen",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-demo.11",
doi = "10.18653/v1/2024.naacl-demo.11",
pages = "107--115",
abstract = "Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without a clear way to incorporate user feedback. To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer{--} an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier{'}sretrieval pipelines and datasets, and extensivelogging of human feedback. To allow users to create and modify effective queries, our demo supports complementary approaches of using LLMs interactively, assisting the user with edits and feedback at multiple stages of the query formulation process. With support for recording fine-grained interactions and user annotations, QueryExplorer can serve as a valuable experimental and research platform for annotation, qualitative evaluation, and conducting Human-in-the-Loop (HITL) experiments for complex search tasks where users struggle to formulate queries.",
}
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<abstract>Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without a clear way to incorporate user feedback. To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer– an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier’sretrieval pipelines and datasets, and extensivelogging of human feedback. To allow users to create and modify effective queries, our demo supports complementary approaches of using LLMs interactively, assisting the user with edits and feedback at multiple stages of the query formulation process. With support for recording fine-grained interactions and user annotations, QueryExplorer can serve as a valuable experimental and research platform for annotation, qualitative evaluation, and conducting Human-in-the-Loop (HITL) experiments for complex search tasks where users struggle to formulate queries.</abstract>
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%0 Conference Proceedings
%T QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration
%A Dhole, Kaustubh
%A Bajaj, Shivam
%A Chandradevan, Ramraj
%A Agichtein, Eugene
%Y Chang, Kai-Wei
%Y Lee, Annie
%Y Rajani, Nazneen
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F dhole-etal-2024-queryexplorer
%X Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without a clear way to incorporate user feedback. To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer– an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier’sretrieval pipelines and datasets, and extensivelogging of human feedback. To allow users to create and modify effective queries, our demo supports complementary approaches of using LLMs interactively, assisting the user with edits and feedback at multiple stages of the query formulation process. With support for recording fine-grained interactions and user annotations, QueryExplorer can serve as a valuable experimental and research platform for annotation, qualitative evaluation, and conducting Human-in-the-Loop (HITL) experiments for complex search tasks where users struggle to formulate queries.
%R 10.18653/v1/2024.naacl-demo.11
%U https://aclanthology.org/2024.naacl-demo.11
%U https://doi.org/10.18653/v1/2024.naacl-demo.11
%P 107-115
Markdown (Informal)
[QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration](https://aclanthology.org/2024.naacl-demo.11) (Dhole et al., NAACL 2024)
ACL
- Kaustubh Dhole, Shivam Bajaj, Ramraj Chandradevan, and Eugene Agichtein. 2024. QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), pages 107–115, Mexico City, Mexico. Association for Computational Linguistics.