arxiv: 2604.10159 · v2 · submitted 2026-04-11 · 💻 cs.CL · cs.DB· cs.IR· cs.MA

Recognition: unknown

ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification

Zhensheng Wang , ZhanTeng Lin , Wenmian Yang , Kun Zhou , Yiquan Zhang , Weijia Jia

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:50 UTC · model grok-4.3

classification 💻 cs.CL cs.DBcs.IRcs.MA

keywords tabular question answeringopen-domain QAunderspecified queriesmulti-turn dialoguemulti-agent frameworkclarificationbenchmarklarge language models

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The pith

A multi-agent framework resolves ambiguities in open-domain tabular questions through multi-turn dialogue clarification.

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

This paper introduces the ODUTQA-MDC task to address how large language models struggle with open-domain tabular questions that have underspecified or uncertain expressions. It provides the first comprehensive benchmark including a dataset of 209 tables with 25,105 QA pairs, a fine-grained labeling scheme for evaluation, and a dynamic clarification interface to simulate user feedback. The authors also propose the MAIC-TQA multi-agent framework designed to detect ambiguities, clarify them via dialogue, and refine answers. A sympathetic reader would care because this advances the ability of AI systems to handle real-world, conversational queries on tabular data that are often incomplete or vague.

Core claim

The paper establishes the ODUTQA-MDC task for open-domain underspecified tabular question answering with multi-turn dialogue-based clarification. It creates a large-scale dataset with 209 tables and 25,105 QA pairs, introduces a fine-grained labeling scheme, and develops a dynamic clarification interface. Additionally, it proposes the MAIC-TQA multi-agent framework that excels at detecting ambiguities, clarifying them through dialogue, and refining answers, with experiments validating the benchmark and framework.

What carries the argument

The MAIC-TQA multi-agent framework, which detects ambiguities in tabular queries, engages in multi-turn dialogue for clarification, and refines answers based on user feedback simulated by the dynamic interface.

If this is right

The benchmark supports detailed evaluation of ambiguity types in tabular data using the fine-grained labeling scheme.
MAIC-TQA shows that multi-agent coordination improves detection and resolution of uncertain expressions in table queries.
The dynamic clarification interface enables controlled testing of interactive refinement loops.
Together these elements provide a foundation for developing more robust conversational tabular QA systems.

Where Pith is reading between the lines

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

The multi-agent clarification pattern could transfer to other structured-data tasks such as spreadsheet analysis or database querying.
The labeling scheme offers a way to generate training signals for models that learn to ask targeted follow-up questions.
Real deployment would still need direct user studies to confirm that simulated feedback matches actual human responses.

Load-bearing premise

The constructed ODUTQA dataset and fine-grained labeling scheme faithfully represent real-world underspecified open-domain tabular queries, and the dynamic clarification interface provides a realistic simulation of user feedback.

What would settle it

An experiment in which human users give substantially different clarifications than the dynamic interface simulates for the same underspecified questions would show that the benchmark does not capture realistic interaction.

Figures

Figures reproduced from arXiv: 2604.10159 by Kun Zhou, Weijia Jia, Wenmian Yang, Yiquan Zhang, ZhanTeng Lin, Zhensheng Wang.

**Figure 2.** Figure 2: General framework of MAIC-TQA. If intent underspecification is detected, the system issues a template-based follow-up question (e.g., “Your input is unclear. Please clarify your request.”) to prompt user clarification. Upon receiving the user’s response, the system concatenates the original query with the clarification to form an updated conversational context, which is then reprocessed by the BERT classi… view at source ↗

**Figure 3.** Figure 3: Examples of Template Filling (Chinese–English Bilingual). stored in a dictionary format without any slots; its purpose is to record the definitive user’s final intent after an underspecified query is clarified. The FROM clarification contains slots related to the table caption, which are populated concurrently with other slots during the data sampling stage. Furthermore, the static text (i.e., the non-… view at source ↗

**Figure 4.** Figure 4: Illustration of an utterance with SLU tags. The example features two intents, where ‘B-C’ denotes ‘B-City’ [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

**Figure 5.** Figure 5: QA example [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗

**Figure 6.** Figure 6: Query rewriting prompts for dataset construction. [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

**Figure 7.** Figure 7: Prompt for sentence rewriting used in the dynamic clarification interface. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗

**Figure 8.** Figure 8: Prompt for slots classification [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt for table caption summarization [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗

**Figure 10.** Figure 10: Prompt for SQL generation and condition underspecification detection. [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗

read the original abstract

The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with open-domain queries exhibiting underspecified or uncertain expressions. To address this, we introduce the ODUTQA-MDC task and the first comprehensive benchmark to tackle it. This benchmark includes: (1) a large-scale ODUTQA dataset with 209 tables and 25,105 QA pairs; (2) a fine-grained labeling scheme for detailed evaluation; and (3) a dynamic clarification interface that simulates user feedback for interactive assessment. We also propose MAIC-TQA, a multi-agent framework that excels at detecting ambiguities, clarifying them through dialogue, and refining answers. Experiments validate our benchmark and framework, establishing them as a key resource for advancing conversational, underspecification-aware Tabular QA research.

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.

Desk Editor's Note private letter to a colleague

The paper carves out a new task for underspecified open-domain tabular QA and ships a dataset plus multi-agent framework, but the reported experiments stay too high-level to judge real progress.

read the letter

The core contribution is a task definition for open-domain tabular questions that are vague or incomplete, paired with a 209-table dataset of 25,105 QA pairs, a fine-grained labeling scheme, and a simulated dialogue interface. They also describe MAIC-TQA, a multi-agent setup meant to spot ambiguities, ask clarifying questions, and update answers. That combination addresses a gap that standard tabular QA benchmarks mostly skip, since most assume fully specified queries. The dataset size and the step-by-step labeling look like useful starting points for anyone building interactive table systems. The multi-agent split is a straightforward way to decompose the clarification problem without forcing everything into one model call. Those pieces are genuinely new relative to prior tabular QA work. The main weakness is the evaluation. The abstract states that experiments validate the benchmark and framework, yet no concrete metrics, baseline comparisons, or error analysis appear in the summary. Without those numbers it is difficult to tell whether MAIC-TQA actually outperforms simpler prompting or retrieval baselines on the new data. The dataset itself is constructed rather than drawn from live user logs, so its match to real underspecification remains an assumption that needs checking. This paper is mainly for groups already working on conversational data interfaces or dialogue-augmented QA. A reader who needs a benchmark for ambiguity handling will find the task framing and interface useful even if the results section needs more detail. I would send it to peer review. The task and data are substantial enough that referees can usefully comment on construction choices and push for fuller experimental reporting.

Referee Report

1 major / 0 minor

Summary. The paper introduces the ODUTQA-MDC task to address open-domain tabular QA challenges with underspecified or uncertain queries. It contributes a benchmark comprising a dataset of 209 tables and 25,105 QA pairs, a fine-grained labeling scheme for evaluation, and a dynamic clarification interface simulating user feedback. The authors also propose the MAIC-TQA multi-agent framework for ambiguity detection, multi-turn clarification, and answer refinement, claiming that experiments validate both the benchmark and framework.

Significance. If the empirical claims hold, this provides a timely new resource for conversational tabular QA research, targeting a clear gap where current LLMs fail on underspecified inputs. The scale of the dataset, the interactive evaluation setup, and the multi-agent clarification approach could become a standard testbed for future work on ambiguity handling in structured-data QA.

major comments (1)

Abstract: the statement that 'Experiments validate our benchmark and framework' is unsupported by any reported metrics, baselines, or error analysis. Without these quantitative details it is impossible to assess whether MAIC-TQA actually excels at ambiguity detection and answer refinement, which is load-bearing for the central claim that the framework advances the state of the art.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation of minor revision. We address the single major comment point-by-point below.

read point-by-point responses

Referee: Abstract: the statement that 'Experiments validate our benchmark and framework' is unsupported by any reported metrics, baselines, or error analysis. Without these quantitative details it is impossible to assess whether MAIC-TQA actually excels at ambiguity detection and answer refinement, which is load-bearing for the central claim that the framework advances the state of the art.

Authors: We agree that the abstract would be strengthened by including key quantitative results. The full manuscript reports detailed experiments (including baselines, accuracy/F1 metrics for ambiguity detection, clarification success rates, and answer refinement improvements) in the Experiments section with error analysis. To make the abstract self-contained and directly support the validation claim, we will revise it to summarize the main empirical findings (e.g., MAIC-TQA's gains over single-agent baselines). revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a new task (ODUTQA-MDC), constructs a dataset (209 tables, 25,105 QA pairs), a fine-grained labeling scheme, a dynamic clarification interface, and proposes the MAIC-TQA multi-agent framework. These are definitional and constructive contributions with no load-bearing derivations, equations, or predictions that reduce by construction to prior self-citations, fitted inputs, or self-defined terms. The central claims rest on the novelty of the benchmark and framework rather than any internal chain that loops back to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper contributes a new task definition and benchmark rather than deriving results from many free parameters or unstated axioms; the main assumptions are standard in the LLM and QA domain.

axioms (1)

domain assumption Large language models can be improved for underspecified tabular QA by using multi-agent dialogue systems.
Underlying the proposal of MAIC-TQA.

invented entities (1)

ODUTQA-MDC task no independent evidence
purpose: To formalize and benchmark open-domain underspecified tabular QA with multi-turn clarification.
Newly introduced task and benchmark.

pith-pipeline@v0.9.0 · 5469 in / 1331 out tokens · 36935 ms · 2026-05-10T15:50:12.363161+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 2 canonical work pages · 2 internal anchors

[1]

InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pages 150–160

Improving generalization in language model- based text-to-sql semantic parsing: Two simple se- mantic boundary-based techniques. InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pages 150–160. Association for Computational Linguistics. Tonghui Re...

2023
[2]

Kimi K2: Open Agentic Intelligence

ACM / Morgan & Claypool. Kimi Team, Yifan Bai, Yiping Bao, and et al. 2025. Kimi k2: Open agentic intelligence.Preprint, arXiv:2507.20534. Zhensheng Wang, Wenmian Yang, Kun Zhou, Yiquan Zhang, and Weijia Jia. 2025. RETQA: A large-scale open-domain tabular question answering dataset for real estate sector. InAAAI-25, Sponsored by the Asso- ciation for the ...

work page internal anchor Pith review arXiv 2025
[3]

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Seq2sql: Generating structured queries from natural language using reinforcement learning. CoRR, abs/1709.00103. A Dataset Construction and Statistics A.1 Template Filling A.1.1 Implementation Details Based on 209 tables, we design 222 seed templates. As shown in Figure 3, these templates are catego- rized by underspecification type: 38 with SELECT unders...

work page internal anchor Pith review arXiv 2023
[4]

Shanghai

Slot Tags (BIO Annotation):We employ the BIO (Begin-Inside-Outside) tagging scheme to identify specific entities that act as filtering constraints in the SQL query. In the visual example, the tokens “Shanghai”, “July”, and “2022” are explicitly tagged asB-C (City), B-M (Month), and B-Y (Year), respectively. Among them, “Shanghai” serves as the table scope...

2022
[5]

forbidden key- words

Intent Labels:Intents capture the semantic goal of the query, determining which columns and operations (such as sorting or aggregation) are required. The example demonstrates a composite intentscenario where the user asks for both volume ranking and price comparison. Consequently, the utterance is labeled with two intents. Role in TQA.In the Table Questio...

2022
[6]

The correct city is [Bei- jing]

Data Retrieval and Template Construc- tion. When the system correctly detects an un- derspecification (e.g., a missing city in the scope), the interface looks up the ground-truth value from the dataset’s clarification dictionary (e.g., FROM_clarification). It then constructs a standard response sentence, such as “The correct city is [Bei- jing].”
[7]

In the dynamic scenario, to simulate the diverse and unpredictable nature of real human language, we employ a LLM (Qwen2.5-72B) to rewrite the standard response

Dynamic Response Generation (LLM-based Rewriting). In the dynamic scenario, to simulate the diverse and unpredictable nature of real human language, we employ a LLM (Qwen2.5-72B) to rewrite the standard response. We utilize a spe- cialized prompt (see Figure 7) that instructs the LLM to act as a grammar expert, making the sen- tence more colloquial and us...
[8]

real” envi- ronment would make it mathematically impossible to fairly compare different models or track progress over time, as the “test set

Reliability Control Mechanism. A critical challenge in automatic generation is ensuring fac- tual consistency. We address this by strict keyword extraction and validation: • Keyword Extraction: Before generation, the system identifies the critical information slot (e.g., "Beijing") that must be present in the response. • Iterative Validation: The generati...
[9]

Unknown domain

The Table Retrieval Agent then uses the prompt shown in Figure 9 to summarize the target table. Finally, the SQL Generation and Validation Agent utilizes the prompt in Figure 10 to generate and validate SQL queries, and utilizes the tool defined by Algorithm 2 to execute SQL statements. Algorithm 1The slotSearchTool used by the SV agent Require:slots: str...
[10]

<Keywords> are the city name, district name, year, month, or time range

<Keywords> listed must not be altered and must appear in the <Rewritten_query>. <Keywords> are the city name, district name, year, month, or time range
[11]

You can only use approximate phrasing

<Forbidden_keywords> listed must not appear in the rewritten query. You can only use approximate phrasing. The intent of the <Rewritten_query> is to phrase a vague question regarding the <Forbidden_keywords>
[12]

Rewriting methods include, but are not limited to, inversion and synonym replacement

You only need to rewrite the question, not answer it. Rewriting methods include, but are not limited to, inversion and synonym replacement
[13]

<Forbidden_keywords>: 'None'

"<Forbidden_keywords>: 'None'" indicates there are no forbidden words
[14]

type:slot

You can be creative and increase sentence diversity while ensuring the meaning remains unchanged, <Keywords> are preserved, and <Forbidden_keywords> do not appear. ############Example 1############## <Query>:Which environment is better when comparing Yuyue Guangnian and Nanan Chaoming in Yuhuatai District, Nanjing City? <Keywords>:['Yuyue Guangnian', 'Nan...
[15]

Extract Target Slots: Filter all slots in <Slots> that match the <Target Slot Types>
[16]

- Combined-slot Verification: Combine target slots (e.g., city + district) according to their order of appearance in <Query>, then perform combined verification

Invoke Tool for Verification: - Single-slot Verification: Search each target slot individually and record the results. - Combined-slot Verification: Combine target slots (e.g., city + district) according to their order of appearance in <Query>, then perform combined verification. ============Step 2: Slot Classification (Apply rules in priority order) ====...
[17]

'Missing': - If a target slot type does not appear at all in <Slots>, classify it as ['', slot_type, 'Missing']
[18]

- Important: Once classified as 'Unmatch', these slots cannot be reclassified as 'Error' or 'Correct'

'Unmatch' (Combination Mismatch): - All individual slot checks return values > 0 (i.e., all exist individually); - But the combination slot query returns 0; - Then both slots in the combination are classified as [slot, slot_type, 'Unmatch']. - Important: Once classified as 'Unmatch', these slots cannot be reclassified as 'Error' or 'Correct'
[19]

'Error' (Retrieval Failed): - If a slot is present in '<Slots>' but its single-slot verification result is 0, classify it as [slot, slot_type, 'Error']
[20]

Please provide the building density of Dongping Town in Shanghai and Yuntai Yuanzhu in Jianye District, Nanjing

'Correct' (Fully Matched): - All target slot types are present; - All individual slot queries return values > 0; - Combined slot query also returns > 0; - Then each involved slot is classified as [slot, slot_type, 'Correct']. ############ [Example] ########## <Query>: "Please provide the building density of Dongping Town in Shanghai and Yuntai Yuanzhu in ...
[21]

system" represents the system's follow-up questions based on the user's original input, and

In the <Dialog>, "system" represents the system's follow-up questions based on the user's original input, and "user" represents the user's supplementary information in response to errors or omissions
[22]

The user’s original query may contain mistakes — you should make judgments based on the user’s supplemental input
[23]

Table of Property Transaction Prices in Haidian District, Beijing

The <Summary> should be a list consisting of a geographic region + table name, such as "Table of Property Transaction Prices in Haidian District, Beijing"; ############ Example 1 ############## <Dialog>: User: Could you tell me how many property units were sold in November 2020 in Park No.1 in Daxing District, Beijing, and Jing'an No.1 in Shanghai? System...

2020
[24]

Analyze the content of the <Dialog> to identify the user's query intent
[25]

Important: Always prioritize the corrected information provided by the user over the erroneous information in the original query
[26]

Generate an SQL query using the provided <SLOTS> and <Table_Captions>
[27]

Enterprise Name

Strictly follow the format shown in the examples below. ############ Example 1 ############## <Dialog>: User: What is the operating profit of Lishui Economic Development Group? System: Your input is missing the year. Please provide a year between 2019 and 2022. User: I'm interested in the year 2021. <Domain>: enterpriseFinanceField <SLOTS>: {'Lishui Econo...

2019