QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912719666413568 |
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| author | Bigdeli, Amin Rad, Radin Hamidi Incesu, Mert Arabzadeh, Negar Clarke, Charles L. A. Bagheri, Ebrahim |
| author_facet | Bigdeli, Amin Rad, Radin Hamidi Incesu, Mert Arabzadeh, Negar Clarke, Charles L. A. Bagheri, Ebrahim |
| contents | We present QueryGym, a lightweight, extensible Python toolkit that supports large language model (LLM)-based query reformulation. This is an important tool development since recent work on llm-based query reformulation has shown notable increase in retrieval effectiveness. However, while different authors have sporadically shared the implementation of their methods, there is no unified toolkit that provides a consistent implementation of such methods, which hinders fair comparison, rapid experimentation, consistent benchmarking and reliable deployment. QueryGym addresses this gap by providing a unified framework for implementing, executing, and comparing llm-based reformulation methods. The toolkit offers: (1) a Python API for applying diverse LLM-based methods, (2) a retrieval-agnostic interface supporting integration with backends such as Pyserini and PyTerrier, (3) a centralized prompt management system with versioning and metadata tracking, (4) built-in support for benchmarks like BEIR and MS MARCO, and (5) a completely open-source extensible implementation available to all researchers. QueryGym is publicly available at https://github.com/radinhamidi/QueryGym. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation Bigdeli, Amin Rad, Radin Hamidi Incesu, Mert Arabzadeh, Negar Clarke, Charles L. A. Bagheri, Ebrahim Information Retrieval Computation and Language We present QueryGym, a lightweight, extensible Python toolkit that supports large language model (LLM)-based query reformulation. This is an important tool development since recent work on llm-based query reformulation has shown notable increase in retrieval effectiveness. However, while different authors have sporadically shared the implementation of their methods, there is no unified toolkit that provides a consistent implementation of such methods, which hinders fair comparison, rapid experimentation, consistent benchmarking and reliable deployment. QueryGym addresses this gap by providing a unified framework for implementing, executing, and comparing llm-based reformulation methods. The toolkit offers: (1) a Python API for applying diverse LLM-based methods, (2) a retrieval-agnostic interface supporting integration with backends such as Pyserini and PyTerrier, (3) a centralized prompt management system with versioning and metadata tracking, (4) built-in support for benchmarks like BEIR and MS MARCO, and (5) a completely open-source extensible implementation available to all researchers. QueryGym is publicly available at https://github.com/radinhamidi/QueryGym. |
| title | QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2511.15996 |