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arxiv: 2606.11945 · v1 · pith:C6WOUVP4new · submitted 2026-06-10 · 💻 cs.CL · cs.IR

uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking

Simon Lupart , Kidist Amde Mekonnen , Zahra Abbasiantaeb , Mohammad Aliannejadi This is my paper

Pith reviewed 2026-06-27 10:11 UTC · model grok-4.3

classification 💻 cs.CL cs.IR
keywords multi-turn retrievalretrieval-augmented generationlearned sparse retrievallistwise rerankingconversational question answeringunanswerable queriesSemEval task
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The pith

A multi-turn RAG pipeline uses learned sparse retrieval and LLM listwise reranking to integrate full conversation history across four domains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a system for SemEval-2026 Task 8 that performs multi-turn retrieval and question answering over collections in finance, cloud documentation, government, and Wikipedia. It relies on learned sparse retrieval to fetch evidence without domain-specific tuning and uses LLMs to rewrite queries, perform pointwise and listwise reranking, and generate answers, with every step conditioned on the complete conversational history. The design explicitly addresses unanswerable queries where the collection lacks sufficient evidence. The central goal is to show that this staged integration of context produces more robust results than single-step approaches.

Core claim

The multi-step design enables effective integration of conversational context throughout retrieval and generation, improving robustness across domains.

What carries the argument

Multi-turn retrieval-augmented generation pipeline that applies learned sparse retrieval first, then LLM-based query rewriting, pointwise and listwise reranking, and final generation, each conditioned on full conversational history.

If this is right

  • Sparse retrieval serves as the primary method because it generalizes without per-domain training.
  • LLM long-context handling allows rewriting, reranking, and generation to use the entire conversation history at once.
  • The pipeline can identify unanswerable queries by checking whether retrieved evidence is sufficient.
  • Listwise reranking selects better passages than retrieval scores alone for the generation step.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same staged pipeline could be tested on other conversational retrieval benchmarks that include unanswerable questions.
  • Removing any single LLM step (rewriting or listwise reranking) and measuring the drop would isolate which component drives the claimed robustness.
  • The approach leaves open whether the same gains appear when the underlying LLM is smaller or when retrieval is restricted to shorter contexts.

Load-bearing premise

Learned sparse retrieval generalizes strongly across the four domains without domain-specific adaptation and LLM listwise reranking measurably improves end-to-end performance.

What would settle it

Measurements on the task test set showing that a domain-adapted dense retriever or a simpler pointwise reranker produces higher final answer quality than the reported pipeline on at least two of the four domains.

Figures

Figures reproduced from arXiv: 2606.11945 by Kidist Amde Mekonnen, Mohammad Aliannejadi, Simon Lupart, Zahra Abbasiantaeb.

Figure 1
Figure 1. Figure 1: Overview of our submission. Early stages rely [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Response generation performance for different [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Retrieval Performance at varying depths. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Response Generation at varying depths [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original abstract

This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems across four domains (finance, cloud documentation, government, Wikipedia), and includes unanswerable queries where the available collection does not contain sufficient evidence to produce a complete response. We propose a multi-turn retrieval-augmented generation pipeline that combines learned sparse retrieval with LLM-based reranking and generation. Using sparse retrieval as the primary retrieval method, we leverage its strong generalization across domains. In addition, we make use of the long-context capabilities of LLMs for conversational query rewriting, pointwise and listwise reranking, and generating the final response, each conditioned on the full conversational history. This multi-step design enables effective integration of conversational context throughout retrieval and generation, improving robustness across domains.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper describes the uva-irlab-conv participation in SemEval-2026 Task 8 on multi-turn retrieval-augmented generation and QA. It proposes a pipeline that uses learned sparse retrieval as the primary retriever, combined with LLM-based conversational query rewriting, pointwise and listwise reranking, and final answer generation, all conditioned on full conversational history, and claims that this design enables effective context integration and improves robustness across the four evaluation domains (finance, cloud documentation, government, Wikipedia) while handling unanswerable queries.

Significance. If the claimed robustness gains were demonstrated through evaluation, the work would provide a concrete example of combining sparse retrieval generalization with LLM context handling for conversational QA; however, the complete absence of any metrics, ablations, or comparisons leaves the significance of the design choices unevaluated.

major comments (2)
  1. [Abstract] Abstract: the assertion that the multi-step design 'improves robustness across domains' is load-bearing for the paper's contribution yet is unsupported by any retrieval metrics (e.g., nDCG, recall), ablation results, baseline comparisons (dense retrieval, single-turn systems), or per-domain breakdowns, rendering the generalization and improvement claims unevaluable.
  2. [The manuscript as a whole] The manuscript provides no experimental section or results table reporting official task scores, comparison against other participants, or analysis of the contribution of listwise reranking versus pointwise reranking or full-history conditioning.
minor comments (2)
  1. [Abstract] The description of the four domains and the unanswerable-query handling would be clearer if accompanied by concrete examples of query rewriting or reranking prompts.
  2. Standard SemEval system papers typically include the team's official ranking and primary metric values; their omission here weakens the report's utility to the shared-task community.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the feedback on our system description paper. We address the major comments point by point below, noting that this is a concise participation report for a shared task.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that the multi-step design 'improves robustness across domains' is load-bearing for the paper's contribution yet is unsupported by any retrieval metrics (e.g., nDCG, recall), ablation results, baseline comparisons (dense retrieval, single-turn systems), or per-domain breakdowns, rendering the generalization and improvement claims unevaluable.

    Authors: We agree that the abstract makes an unsupported claim about robustness improvements. The statement was intended to reflect the design rationale—leveraging learned sparse retrieval for cross-domain generalization and LLM conditioning on full history—but no quantitative evidence is provided in the manuscript. We will revise the abstract to remove this claim and describe the pipeline components without asserting empirical gains. revision: yes

  2. Referee: [The manuscript as a whole] The manuscript provides no experimental section or results table reporting official task scores, comparison against other participants, or analysis of the contribution of listwise reranking versus pointwise reranking or full-history conditioning.

    Authors: This manuscript is a system description focused on the pipeline architecture rather than a full experimental study. Official task scores are aggregated in the SemEval task overview rather than individual reports, and we did not run the requested ablations or comparisons during participation. We will add a brief results section reporting any available official scores in revision, but component-level analysis is not available from our work. revision: partial

standing simulated objections not resolved
  • The manuscript contains no experimental results, metrics, ablations, or comparisons, which prevents providing the requested evidence or analysis.

Circularity Check

0 steps flagged

No circularity; purely descriptive system report with no derivations or fitted predictions

full rationale

The paper is a participation report for a SemEval shared task. It describes a retrieval-augmented generation pipeline using learned sparse retrieval, LLM query rewriting, pointwise/listwise reranking, and response generation, all conditioned on conversational history. No equations, parameters, derivations, or quantitative predictions appear in the provided text. Claims about generalization and robustness are presented as design motivations rather than results derived from prior steps within the paper. No self-citations, ansatzes, or renamings reduce any claim to its own inputs by construction. The work is self-contained as an engineering description.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model, free parameters, axioms, or invented entities are present; the paper is an applied system description for a shared task.

pith-pipeline@v0.9.1-grok · 5695 in / 1187 out tokens · 27119 ms · 2026-06-27T10:11:54.611056+00:00 · methodology

discussion (0)

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Reference graph

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