LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL Queries

Jiangqi Huang; Qiang Duan; Shijing Hu; Xuancheng Ren; Zhihui Lu

arxiv: 2601.10398 · v3 · submitted 2026-01-15 · 💻 cs.AI

LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL Queries

Xuancheng Ren , Shijing Hu , Zhihui Lu , Jiangqi Huang , Qiang Duan This is my paper

Pith reviewed 2026-05-16 14:04 UTC · model grok-4.3

classification 💻 cs.AI

keywords text-to-SQLunanswerable querieslatent signalsrefusal mechanismhidden activationsanswerability predictionLLM safetygated encoder

0 comments

The pith

LatentRefusal predicts whether a text-to-SQL query is answerable by examining intermediate hidden activations in large language models.

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

Text-to-SQL systems often produce misleading or unsafe results when users ask unanswerable or underspecified questions. Current refusal methods either follow brittle output instructions that models can hallucinate around or add expensive uncertainty calculations. LatentRefusal instead reads signals directly from the model's internal hidden states during processing. It uses a lightweight Tri-Residual Gated Encoder to filter out schema noise and highlight sparse cues of mismatch between the question and database schema. This approach adds almost no overhead while improving detection accuracy across multiple benchmarks.

Core claim

The central claim is that answerability can be reliably predicted from intermediate hidden activations using the Tri-Residual Gated Encoder, which suppresses schema noise and amplifies localized cues of question-schema mismatch, providing an attachable safety layer that achieves 88.5% average F1 with only 2 milliseconds of added probe time across four benchmarks.

What carries the argument

The Tri-Residual Gated Encoder, a lightweight probing architecture that isolates sparse, localized cues of unanswerability from intermediate hidden activations by suppressing schema noise.

If this is right

Improves average F1 score to 88.5% on answerability prediction for both tested backbones.
Adds only about 2 milliseconds of overhead per query as an attachable module.
Works effectively across diverse ambiguous and unanswerable query settings in four benchmarks.
Avoids reliance on output-level instruction following or output uncertainty estimation.
Provides a more robust safety mechanism against generating executable but misleading SQL programs.

Where Pith is reading between the lines

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

Similar latent-signal approaches could extend to detecting other failure modes in LLM applications beyond text-to-SQL.
Integration with existing text-to-SQL pipelines could become standard for production safety without retraining the base model.
Interpretability analyses suggest potential for visualizing specific mismatch cues that the probe detects.
The method's efficiency makes it suitable for real-time deployment in interactive database query interfaces.

Load-bearing premise

The assumption that reliable cues of question-schema mismatch appear as sparse localized signals in the intermediate hidden activations and can be isolated without being overwhelmed by other model behaviors.

What would settle it

If the Tri-Residual Gated Encoder probe fails to outperform output-based methods on a new set of carefully constructed ambiguous queries where the model hallucinates answerability, or if ablation shows no gain when removing the residual gating components.

Figures

Figures reproduced from arXiv: 2601.10398 by Jiangqi Huang, Qiang Duan, Shijing Hu, Xuancheng Ren, Zhihui Lu.

**Figure 1.** Figure 1: Comparison of refusal paradigms. Top: Traditional prompt-based methods rely on the LLM’s output, which often fails under uncertainty or hallucination. Bottom: Our approach detects refusal signals directly from the frozen LLM’s internal hidden states before generation, ensuring a safe and efficient refusal mechanism without generating or executing any SQL. This enables a single-pass, low-latency refusal de… view at source ↗

**Figure 2.** Figure 2: Overview of LATENTREFUSAL. (a) Refusal gating: given the question and schema, a frozen base LLM produces hidden states; a lightweight probe predicts answerability before any SQL is generated, and a binary gate either triggers SQL generation or returns a safe refusal. (b) TRGE probe: a Tri-Residual Gated Encoder layer augments a standard Transformer block with an additional SwiGLU-gated residual branch to s… view at source ↗

**Figure 3.** Figure 3: Running screenshot of LATENTREFUSAL in a financial deployment. The system correctly identifies a complex, constraint-heavy query as answerable (p = 0.996) while rejecting a subjective, out-of-scope research request (p = 0.000). Inference latency is stable (≈ 467ms). between the query constraints and the database schema, assigning a high answerability probability (p = 0.996). This demonstrates that the prob… view at source ↗

read the original abstract

In LLM-based text-to-SQL systems, unanswerable and underspecified user queries may generate not only incorrect text but also executable programs that yield misleading results or violate safety constraints, posing a major barrier to safe deployment. Existing refusal strategies for such queries either rely on output-level instruction following, which is brittle due to model hallucinations, or estimate output uncertainty, which adds complexity and overhead. To address this challenge, we formalize safe refusal in text-to-SQL systems as an answerability-gating problem and propose LatentRefusal, a latent-signal refusal mechanism that predicts query answerability from intermediate hidden activations of a large language model. We introduce the Tri-Residual Gated Encoder, a lightweight probing architecture, to suppress schema noise and amplify sparse, localized cues of question-schema mismatch that indicate unanswerability. Extensive empirical evaluations across diverse ambiguous and unanswerable settings, together with ablation studies and interpretability analyses, demonstrate the effectiveness of the proposed approach and show that LatentRefusal provides an attachable and efficient safety layer for text-to-SQL systems. Across four benchmarks, LatentRefusal improves average F1 to 88.5 percent on both backbones while adding approximately 2 milliseconds of probe overhead.

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 introduces a latent-space probe using a Tri-Residual Gated Encoder to detect unanswerable text-to-SQL queries before generation, with reported F1 gains to 88.5% and low overhead.

read the letter

The main takeaway is that LatentRefusal shifts refusal detection into the model's intermediate activations rather than relying on output instructions or uncertainty estimates. This gives an attachable safety layer that adds roughly 2 ms of overhead while lifting average F1 to 88.5% across four benchmarks on two backbones. The Tri-Residual Gated Encoder is the concrete new piece: it tries to suppress schema noise and pull out sparse mismatch signals that mark unanswerable queries. The ablations and interpretability checks in the paper directly test whether those signals can be isolated, and the numbers suggest the mechanism works better than the baselines they compare against. That is the part worth paying attention to if you care about practical deployment constraints in text-to-SQL systems. The central assumption—that mismatch cues reliably appear as localized patterns in hidden states—gets some support from the reported analyses, though it still rests on the training data containing enough clear unanswerable examples. If the full experiments control for data splits and avoid leakage between the probe and the main model, the claim holds up; the abstract alone leaves that open. The work is aimed at people building or hardening LLM database interfaces who need something lighter than full output verification. It is not a theoretical advance in refusal or representation learning, but it is a clean engineering step that could be reproduced or extended. I would send it to peer review because the empirical package looks complete enough for referees to check the controls and the overhead claims.

Referee Report

1 major / 1 minor

Summary. The paper formalizes safe refusal in LLM-based text-to-SQL systems as an answerability-gating problem and proposes LatentRefusal, which predicts query answerability from intermediate hidden activations using a lightweight Tri-Residual Gated Encoder to suppress schema noise and amplify sparse question-schema mismatch cues. It reports average F1 of 88.5% across four benchmarks on two backbones, with ablation studies, interpretability analyses, and approximately 2 ms probe overhead, positioning the method as an attachable safety layer.

Significance. If the results hold, the approach offers an efficient alternative to brittle output-level instruction following or high-overhead uncertainty estimation, providing a practical safety mechanism for text-to-SQL deployment. The latent-signal probing and gated encoder design could extend to other LLM safety tasks where early detection of unanswerability is valuable.

major comments (1)

Experimental section: The abstract and results claim F1 improvements to 88.5% and effectiveness via ablations, but provide no details on baselines, statistical significance tests, data splits, or controls for confounds such as query length or schema complexity; this information is load-bearing for verifying the central claim that the Tri-Residual Gated Encoder reliably isolates mismatch cues.

minor comments (1)

Abstract: The overhead figure of 'approximately 2 milliseconds' should specify the measurement hardware, batch size, and exact timing methodology for reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comments and for recognizing the potential of LatentRefusal as an efficient attachable safety layer. We address the major comment on experimental details below and will incorporate the requested clarifications in the revised manuscript.

read point-by-point responses

Referee: [—] Experimental section: The abstract and results claim F1 improvements to 88.5% and effectiveness via ablations, but provide no details on baselines, statistical significance tests, data splits, or controls for confounds such as query length or schema complexity; this information is load-bearing for verifying the central claim that the Tri-Residual Gated Encoder reliably isolates mismatch cues.

Authors: The manuscript describes the baselines (output-level instruction following and uncertainty estimation) and the four benchmarks with their standard data splits in Section 4. We agree, however, that statistical significance tests and explicit controls for confounds such as query length and schema complexity were not reported in sufficient detail. We will revise the experimental section to add McNemar’s tests for F1 differences, plus stratified results and regression controls by query length bins and schema complexity metrics (number of tables/columns). These additions will directly support the claim that the Tri-Residual Gated Encoder isolates mismatch cues beyond these factors. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper formalizes refusal as an answerability-gating task and introduces the Tri-Residual Gated Encoder as a lightweight probe on intermediate LLM activations. Its central claims rest on empirical F1 improvements across four benchmarks, ablation studies, and interpretability analyses that directly test cue isolation. No derivation step equates a prediction to its own fitted inputs by construction, no load-bearing premise collapses to a self-citation chain, and no ansatz or uniqueness result is smuggled in; the architecture and evaluations remain independent of the target refusal labels.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach rests on the domain assumption that answerability signals exist in hidden states and can be extracted by the new encoder; no free parameters or invented entities beyond the proposed architecture are detailed in the abstract.

axioms (1)

domain assumption Intermediate hidden activations contain detectable and probeable signals indicating query answerability or unanswerability
This is the core premise enabling the shift from output-level to latent-signal refusal.

invented entities (1)

Tri-Residual Gated Encoder no independent evidence
purpose: Suppress schema noise and amplify sparse cues of question-schema mismatch
New lightweight probing architecture introduced to implement the latent refusal mechanism.

pith-pipeline@v0.9.0 · 5523 in / 1256 out tokens · 61396 ms · 2026-05-16T14:04:57.063789+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 2 internal anchors

[1]

Xiang Du, Chen Xiao, and Yang Li

PRACTIQ: A practical conversational text- to-SQL dataset with ambiguous and unanswerable queries.arXiv preprint arXiv:2410.11076. Xiang Du, Chen Xiao, and Yang Li. 2024. Haloscope: Harnessing unlabeled LLM generations for halluci- nation detection.arXiv preprint arXiv:2409.17504. Ran El-Yaniv and Yair Wiener. 2010. On the founda- tions of noise-free selec...

work page arXiv 2024
[2]

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.arXiv preprint arXiv:2311.05232. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hal- lucination in natural language generation.ACM Computing Surveys, 55(12):1...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[3]

A survey of NL2SQL with large language models – where are we, and where are we going?arXiv preprint arXiv:2408.05109v1,

A survey of text-to-sql in the era of llms.arXiv preprint arXiv:2408.05109. 9 Potsawee Manakul, Adian Liusie, and Mark J. F. Gales

work page arXiv
[4]

SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Selfcheckgpt: Zero-resource black-box hal- lucination detection for generative large language models.arXiv preprint arXiv:2303.08896. Samuel Marks and Max Tegmark. 2024. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In Conference on Language Modeling (COLM). Sung-Min Park, Xue-Ying Du, Min...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[5]

Query a specific historical data point, e.g., price or volume at a certain time

work page
[6]

Perform simple statistical computations on historical data, e.g., average, sum, max/min

work page
[7]

Retrieve records that satisfy specific conditions, e.g., values above a threshold

work page
[8]

Query basic attributes or identifiers of an entity, e.g., code or name

work page
[9]

Unanswerable questions usually have the following characteristics:

Perform the above within a specified time range. Unanswerable questions usually have the following characteristics:

work page
[10]

No concrete and direct computational logic (e.g., How to analyze employees’ promotion paths?)

work page
[11]

Future prediction or trend judgment (e.g., Will the employee resign in the future?)

work page
[12]

Subjective analysis or evaluation (e.g., How is the employee’s work capability?)

work page
[13]

Require information beyond the database

work page
[14]

Open-ended or advice-seeking questions (e.g., How to evaluate the employee’s performance?)

work page
[15]

Decision-making guidance

work page
[16]

Require causal explanation

work page
[17]

Require real-time or dynamic data

work page
[18]

label": boolean,

Require deep analysis or complex models. When judging, consider whether the question has a clear answer and whether the answer can be derived solely from the existing historical data. If the question is vague or requires additional information and analysis, it should be judged as unanswerable. ### Output in JSON format: { "label": boolean, "tables": [ { "...

work page 2048

[1] [1]

Xiang Du, Chen Xiao, and Yang Li

PRACTIQ: A practical conversational text- to-SQL dataset with ambiguous and unanswerable queries.arXiv preprint arXiv:2410.11076. Xiang Du, Chen Xiao, and Yang Li. 2024. Haloscope: Harnessing unlabeled LLM generations for halluci- nation detection.arXiv preprint arXiv:2409.17504. Ran El-Yaniv and Yair Wiener. 2010. On the founda- tions of noise-free selec...

work page arXiv 2024

[2] [2]

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.arXiv preprint arXiv:2311.05232. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hal- lucination in natural language generation.ACM Computing Surveys, 55(12):1...

work page internal anchor Pith review Pith/arXiv arXiv 2023

[3] [3]

A survey of NL2SQL with large language models – where are we, and where are we going?arXiv preprint arXiv:2408.05109v1,

A survey of text-to-sql in the era of llms.arXiv preprint arXiv:2408.05109. 9 Potsawee Manakul, Adian Liusie, and Mark J. F. Gales

work page arXiv

[4] [4]

SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Selfcheckgpt: Zero-resource black-box hal- lucination detection for generative large language models.arXiv preprint arXiv:2303.08896. Samuel Marks and Max Tegmark. 2024. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In Conference on Language Modeling (COLM). Sung-Min Park, Xue-Ying Du, Min...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[5] [5]

Query a specific historical data point, e.g., price or volume at a certain time

work page

[6] [6]

Perform simple statistical computations on historical data, e.g., average, sum, max/min

work page

[7] [7]

Retrieve records that satisfy specific conditions, e.g., values above a threshold

work page

[8] [8]

Query basic attributes or identifiers of an entity, e.g., code or name

work page

[9] [9]

Unanswerable questions usually have the following characteristics:

Perform the above within a specified time range. Unanswerable questions usually have the following characteristics:

work page

[10] [10]

No concrete and direct computational logic (e.g., How to analyze employees’ promotion paths?)

work page

[11] [11]

Future prediction or trend judgment (e.g., Will the employee resign in the future?)

work page

[12] [12]

Subjective analysis or evaluation (e.g., How is the employee’s work capability?)

work page

[13] [13]

Require information beyond the database

work page

[14] [14]

Open-ended or advice-seeking questions (e.g., How to evaluate the employee’s performance?)

work page

[15] [15]

Decision-making guidance

work page

[16] [16]

Require causal explanation

work page

[17] [17]

Require real-time or dynamic data

work page

[18] [18]

label": boolean,

Require deep analysis or complex models. When judging, consider whether the question has a clear answer and whether the answer can be derived solely from the existing historical data. If the question is vague or requires additional information and analysis, it should be judged as unanswerable. ### Output in JSON format: { "label": boolean, "tables": [ { "...

work page 2048