---
title: "uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with"
description: "This paper describes the University of Amsterdam team's system for SemEval-2026 Task 8, which is… 42 references, cited by 1 paper here."
authors: ["Simon Lupart", "Kidist Mekonnen", "Zahra Abbasiantaeb", "Mohammad Aliannejadi"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.373/"
doi: "10.18653/v1/2026.semeval-1.373"
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/lupart-2026-uva-irlab-conv-semeval-2026/"
---
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# uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking

**Authors:** [Simon Lupart](https://md.research.elamur.ai/free/en/authors/simon-lupart/), [Kidist Mekonnen](https://md.research.elamur.ai/free/en/authors/kidist-amde-mekonnen/), Zahra Abbasiantaeb and [Mohammad Aliannejadi](https://md.research.elamur.ai/free/en/authors/mohammad-aliannejadi/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **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/2026.semeval-1.373/) · [PDF](https://aclanthology.org/2026.semeval-1.373.pdf) · [DOI 10.18653/v1/2026.semeval-1.373](https://doi.org/10.18653/v1/2026.semeval-1.373) · [OpenAlex](https://openalex.org/W7166821390)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/lupart-2026-uva-irlab-conv-semeval-2026/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This paper describes the University of Amsterdam team's system for SemEval-2026 Task 8, which is built on the MTRAGEval benchmark. The task covers conversational retrieval and answer generation over four English collections: ClapNQ (Wikipedia), FiQA (finance), government, and IBM Cloud documentation. Some turns cannot be answered from the available collection. The authors use a cascading zero-shot pipeline:

- GPT-4.1 turns the latest user turn into a standalone query.
- The learned sparse retriever LION-SP-8B returns 1000 candidates.
- Qwen3-Reranker-8B rescores them pointwise and keeps the top 20.
- GPT-4.1 reranks these listwise into a top 10, using the whole dialogue history.
- GPT-4.1 writes the answer from the five best passages.

None of the components were tuned to the task domains. The system took 2nd place out of 38 teams in retrieval. Generation ranked lower because unanswerable turns were not explicitly handled, but the answers stayed highly faithful to the evidence. The work shows that sparse retrieval plus context-aware LLM reranking transfers well across domains.

## Key points
- Task A retrieval reached nDCG@5 = 0.5475, ranking 2nd of 38. Retrieval alone scored 0.4841, already above the organizers' baseline (GPT-OSS-20B rewriting + ELSER, 0.4795). Listwise reranking mainly lifted nDCG@1, from 0.5120 to 0.5331.
- nDCG@5 by domain: ClapNQ 0.645, Govt 0.587, IBM Cloud 0.552, FiQA 0.330. Finance was the hardest domain, over 332 evaluated turns in total.
- Generation ranked 23/26 on Task B (oracle passages) and 20/29 on Task C (RAG). Without IDK conditioning, the harmonic mean reached 0.5619 and faithfulness (RL_F) was 0.8035.
- Retrieval quality and answer quality correlate (Pearson r = 0.46). Faithfulness, however, shows no link to nDCG.
- Generation quality drops as conversations get longer, while retrieval stays stable. Sparse search uses the Seismic library with pruning thresholds of (400, 600).

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

## Institutions

- [University of Amsterdam](https://md.research.elamur.ai/free/en/universities/university-of-amsterdam/) — University, Amsterdam, Netherlands · 144 works · 173,906 citations

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🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 473 works

## Cite

Cite as: Simon Lupart, Kidist Mekonnen, Zahra Abbasiantaeb and Mohammad Aliannejadi. 2026. uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

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

```bibtex
@inproceedings{lupart2026uva,
  title = {{uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking}},
  author = {Simon Lupart, Kidist Mekonnen, Zahra Abbasiantaeb and Mohammad Aliannejadi},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.373},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.373}
}
```

- [← previous UTRAG at SemEval-2026 Task 8: History-Aware Query Rewriting and LoRA-Finetuned Generation for Multi-Turn RAG](https://md.research.elamur.ai/free/en/papers/zhou-2026-utrag-semeval-2026-task-8/)
- [next → V-GameGym: Visual Game Generation for Code Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-v-gamegym-visual-game-generation/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/19/)

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*Updated 31 October 2026*