---
title: "AdaCQR — paper by Lai et al. (2025) — summary and ideas"
description: "Conversational search needs to turn a context-dependent user question, often with omissions… 61 references, cited by 9 papers here."
authors: ["Yilong Lai", "Jialong Wu", "Congzhi Zhang", "Haowen Sun", "Deyu Zhou"]
year: 2025
venue: "Proceedings of the 31st International Conference on Computational Linguistics"
source: "https://aclanthology.org/2025.coling-main.515/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lai-2025-adacqr-enhancing-query-reformulation-conversational/"
---
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# AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment

**Authors:** [Yilong Lai](https://md.research.elamur.ai/free/en/authors/yilong-lai/), [Jialong Wu](https://md.research.elamur.ai/free/en/authors/jialong-wu/), [Congzhi Zhang](https://md.research.elamur.ai/free/en/authors/congzhi-zhang/), Haowen Sun and [Deyu Zhou](https://md.research.elamur.ai/free/en/authors/deyu-zhou/) · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.515/) · [PDF](https://aclanthology.org/2025.coling-main.515.pdf) · [OpenAlex](https://openalex.org/W4400377804)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/lai-2025-adacqr-enhancing-query-reformulation-conversational/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Conversational search needs to turn a context-dependent user question, often with omissions, coreference or ambiguity, into a self-contained query that standard retrievers can handle. Earlier alignment-based conversational query reformulation (CQR) methods optimize for one retriever, so their rewrites may transfer poorly to others. AdaCQR targets sparse (term-based, e.g. BM25) and dense (semantic) retrieval together. A fusion metric scores how well a rewritten query works across both retriever types. The authors use uncertainty estimates from this metric to select representative examples, which steer an LLM toward better reformulation labels than costly human rewrites. Training has two stages. First, a lightweight model is fine-tuned with cross-entropy on these labels. Then Diverse Beam Search produces several candidate rewrites, the fusion metric ranks them, and a contrastive loss teaches the model their order. This avoids the instability and reference-model dependence of RL- or MBR-based alignment. On TopiOCQA, QReCC and TREC CAsT, AdaCQR outperforms prior methods and rivals systems built on LLaMA-7B or proprietary LLMs. This shows that retriever-agnostic rewriting can be achieved cheaply.

## Key points
- The fusion metric combines term overlap (key entities) and semantic query–passage similarity. It is used both to obtain superior labels and to order candidate queries for reference-free evaluation.
- In the two-stage training, stage 1 uses a generation loss (teacher forcing) and stage 2 uses a contrastive loss over Diverse Beam Search candidates. The same model acts as generator and evaluator.
- The method is evaluated on TopiOCQA, QReCC and TREC CAsT (2019–2021 editions), with both BM25 and dense retrieval.
- An optional LLM-based query expansion (generated answers plus keywords) can rescue cases where the plain rewrite fails, e.g. TopiOCQA case 55_11 with BM25.
- Code is available at github.com/init0xyz/AdaCQR (COLING 2025, pp. 7698–7720).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yilong Lai, Jialong Wu, Congzhi Zhang, Haowen Sun and Deyu Zhou. 2025. AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{lai2025adacqr,
  title = {{AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment}},
  author = {Yilong Lai, Jialong Wu, Congzhi Zhang, Haowen Sun and Deyu Zhou},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.515/}
}
```

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*Updated 31 October 2026*