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HFEPX · Eval paper review

Probing Ambiguity: Adversarial Intent Resolution for Robust Text-to-SQL

Youbing Yin, Xujun Peng, Muhammad S. Uddin, Erin Babinsky +2 more

Published

Oct 16, 2026

Citations

0

Trust level

Low

Usefulness score

25/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 16, 2026

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Tags

Automatic Metrics Multi Agent Multilingual

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
25/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations. In practice, however, many queries are inherently ambiguous, admitting multiple valid interpretations that yield distinct outputs. We introduce Adversarial Intent Resolution (AIR), a multi-agent framework that actively probes and resolves such ambiguity without any task-specific training. AIR first constructs competing interpretations, verifies through SQL execution that they produce genuinely different results, and generates targeted clarification questions grounded in the observed data differences. Evaluating on leading benchmarks for ambiguous questions, AIR demonstrates strong performance, efficiency, and robust cross-benchmark transferability. On the AMBROSIA benchmark under schema-only context, a realistic production setting where exposing the full database is infeasible, AIR outperforms IntentRL, the state-of-the-art reinforcement learning model, across all metrics (e.g., F1 0.679 vs. 0.618). Crucially, AIR exhibits only ~7% performance degradation without the full database, compared to ~20% for IntentRL. On the AmbiQT benchmark, where ambiguity arises from schema design rather than linguistic structure, AIR achieves higher F1 under both context settings without any benchmark-specific adaptation.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations."

Reported Metrics

partial

F1

Useful for evaluation criteria comparison.

"Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

f1
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Multilingual
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Multi Agent
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations.
  • In practice, however, many queries are inherently ambiguous, admitting multiple valid interpretations that yield distinct outputs.
  • We introduce Adversarial Intent Resolution (AIR), a multi-agent framework that actively probes and resolves such ambiguity without any task-specific training.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

F1

Contribution summary

  • We introduce Adversarial Intent Resolution (AIR), a multi-agent framework that actively probes and resolves such ambiguity without any task-specific training.
  • On the AMBROSIA benchmark under schema-only context, a realistic production setting where exposing the full database is infeasible, AIR outperforms IntentRL, the state-of-the-art reinforcement learning model, across all metrics (e.g., F1…
  • On the AmbiQT benchmark, where ambiguity arises from schema design rather than linguistic structure, AIR achieves higher F1 under both context settings without any benchmark-specific adaptation.

Why it matters for eval

  • We introduce Adversarial Intent Resolution (AIR), a multi-agent framework that actively probes and resolves such ambiguity without any task-specific training.
  • On the AMBROSIA benchmark under schema-only context, a realistic production setting where exposing the full database is infeasible, AIR outperforms IntentRL, the state-of-the-art reinforcement learning model, across all metrics (e.g., F1…

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: f1