arxiv: 2605.07883 · v1 · submitted 2026-05-08 · 💻 cs.CL

Recognition: no theorem link

Beyond "I cannot fulfill this request": Alleviating Rigid Rejection in LLMs via Label Enhancement

Ying Zhang , Congyu Qiao , Xin Geng , Ning Xu

Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:50 UTC · model grok-4.3

classification 💻 cs.CL

keywords large language modelssafety alignmentrigid rejectionlabel enhancementvariational inferencerefusal mechanismsnatural responses

0 comments

The pith

Label enhancement via variational inference lets LLMs refuse harmful requests with nuanced, natural language instead of rigid templates.

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

Large language models use safety alignment to refuse dangerous prompts, but this often produces repetitive refusals like 'I cannot fulfill this request' that feel abrupt and unhelpful. The paper proposes LANCE, a method that applies variational inference to learn a continuous distribution over multiple rejection categories rather than forcing a single binary refusal. These fine-grained distributions supply textual gradients that guide a refinement model to strip out only the hazardous elements while leaving the rest of the prompt intact. Experiments show the resulting responses stay secure yet score higher on helpfulness and naturalness than standard aligned models or other baselines. A sympathetic reader would care because this change could make everyday interactions with AI assistants feel more fluid without lowering safety thresholds.

Core claim

The authors claim that performing label enhancement through variational inference to predict continuous rejection distributions across multiple categories supplies multi-way textual gradients to a refinement model, enabling it to neutralize hazardous content in the original prompt so that the LLM generates safe responses free of rigid rejection phrasing while retaining natural interaction style.

What carries the argument

LANCE, which uses variational inference to produce continuous rejection distributions that guide prompt refinement.

If this is right

LLMs produce safe answers that avoid generic refusal templates.
Responses maintain high security standards while scoring better on naturalness.
The method outperforms existing baselines in both helpfulness and conversational flow.
Fine-grained rejection categories allow more precise removal of hazards than single-label refusals.

Where Pith is reading between the lines

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

The technique could be combined with other alignment methods to handle edge cases in safety tuning.
Wider testing on diverse harmful prompt categories would clarify how well the continuous distributions generalize.
If the refinement step scales efficiently, it might reduce the need for heavy post-training safety filters.

Load-bearing premise

The continuous rejection distributions learned by variational inference will neutralize only the hazardous parts of a prompt without distorting the user's original benign intent or introducing new unsafe material.

What would settle it

A test set of prompts where LANCE-generated responses either contain unsafe content or fail to answer the benign part of the query more often than baseline methods would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.07883 by Congyu Qiao, Ning Xu, Xin Geng, Ying Zhang.

**Figure 1.** Figure 1: Rigid refusal examples. distributions provide multi-way textual gradients, enabling a refinement model to perform refinement on prompts. By specifically targeting and neutralizing the hazardous elements within the prompt, LANCE enables LLMs generate safe responses that avoid rigid rejections while preserving the naturalness of interactions. The main contributions of this paper are summarized as follows: •… view at source ↗

**Figure 2.** Figure 2: Overview of our framework LANCE. By disentangling latent risk into a continuous rejection distribution and applying multi-way textual gradient refinement, our method dynamically transforms unsafe inputs to bridge the gap between rigid safety refusal and user helpfulness. risk category y j and its corresponding intensity d j determine the gradient direction and step size, respectively. To implement such a … view at source ↗

read the original abstract

Large Language Models (LLMs) rely on safety alignment to obey safe requests while refusing harmful ones. However, traditional refusal mechanisms often lead to "rigid rejection," where a general template (e.g., "I cannot fulfill this request") indiscriminately triggers refusals and severely undermines the naturalness of interactions between humans and LLMs. To address this issue, LANCE is proposed in this paper to ensure safe yet flexible and natural responses via label enhancement. Specifically, LANCE employs variational inference to perform label enhancement, predicting a continuous distribution across multiple rejection categories. These fine-grained rejection distributions provide multi-way textual gradients for a refinement model to neutralize the hazardous elements in the prompt, so that the LLMs could generate safe responses that avoid rigid rejections while preserving the naturalness of interactions. Experiments demonstrate that LANCE significantly alleviates the rigid rejection problem while maintaining high security standards, significantly outperforming existing baseline models in terms of helpfulness and naturalness of responses.

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

LANCE uses variational inference to produce continuous rejection distributions that feed a prompt refinement model, but the evaluation lacks the controls needed to back the performance claims.

read the letter

The main point is that this paper describes LANCE, a two-stage method where variational inference learns soft distributions over several rejection categories, and those distributions then supply signals to a refinement model that edits the input prompt to remove hazards while trying to keep the response natural and helpful instead of defaulting to a canned refusal template. The new element is the specific use of variational inference to generate multi-way continuous labels for this kind of downstream editing, rather than sticking with binary classifiers or fixed refusal strings. The paper does a reasonable job laying out the usability problem with current safety alignments and sketching how the label enhancement step could give the refinement model more nuanced guidance than hard refusals allow. That framing is clear and targets a real deployment friction. The soft spots sit mostly in the experiments. The abstract states that LANCE outperforms baselines on helpfulness and naturalness while preserving security, yet it gives no concrete information on dataset construction, how prompts were chosen or balanced, the exact metrics for naturalness, statistical significance, or any ablations that test whether the continuous distributions actually isolate hazards without distorting benign intent or creating new unsafe content. The load-bearing assumption that the variational posterior cleanly separates the dangerous parts is not yet shown to hold under the kinds of checks that would make the outperformance claims convincing. If the full paper supplies those details and controls, the work becomes more solid; without them the gains could be tied to training choices rather than the method itself. This is aimed at engineers and researchers working on practical LLM safety and user-facing alignment. Someone who needs to reduce blunt refusals in production models might find the architecture worth implementing and testing. I would send it for peer review. The idea is straightforward, the motivation is sound, and referees could push for the missing experimental rigor that would let readers judge whether the variational step delivers the promised separation.

Referee Report

2 major / 1 minor

Summary. The paper proposes LANCE to mitigate rigid rejection in safety-aligned LLMs, where generic refusal templates undermine interaction naturalness. LANCE applies variational inference to enhance rejection labels by inferring continuous distributions over multiple rejection categories; these distributions supply multi-way textual gradients to a refinement model that neutralizes hazardous prompt elements while preserving benign intent. The resulting safe responses are claimed to be more helpful and natural than those from baseline models, with experiments demonstrating outperformance on helpfulness and naturalness metrics while maintaining high security standards.

Significance. If the central claims hold after proper validation, the work addresses a practical deployment issue in LLM safety alignment by enabling nuanced, non-template refusals. The variational label-enhancement approach could provide a reusable mechanism for fine-grained safety control, potentially influencing subsequent prompt-refinement and alignment research if the separation of hazardous versus benign content is shown to be reliable.

major comments (2)

[Method] The variational inference procedure for label enhancement (described in the method) lacks an explicit ELBO formulation, posterior approximation details, or ablation studies demonstrating that the learned continuous distributions assign mass only to truly hazardous spans without distorting benign intent or introducing new unsafe content. This assumption is load-bearing for all downstream claims of improved helpfulness and preserved security.
[Experiments] The experimental section provides no information on dataset construction, choice of evaluation metrics for helpfulness/naturalness/security, statistical significance testing, or controls for prompt difficulty and baseline prompt engineering. Without these, the reported outperformance cannot be verified or reproduced.

minor comments (1)

[Abstract] The term 'multi-way textual gradients' is introduced in the abstract and method without a precise definition or derivation showing how the continuous distributions are converted into gradient signals for the refinement model.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript to strengthen the presentation of the method and experiments.

read point-by-point responses

Referee: [Method] The variational inference procedure for label enhancement (described in the method) lacks an explicit ELBO formulation, posterior approximation details, or ablation studies demonstrating that the learned continuous distributions assign mass only to truly hazardous spans without distorting benign intent or introducing new unsafe content. This assumption is load-bearing for all downstream claims of improved helpfulness and preserved security.

Authors: We acknowledge that the current manuscript presents the variational inference component at a conceptual level without the full derivation. In the revised version, we will add the explicit ELBO objective for the label enhancement step, specify the variational posterior approximation (including the form of the distribution over rejection categories), and include ablation studies. These studies will quantify how the inferred continuous distributions focus on hazardous spans (via comparison to annotated subsets and safety classifiers) while preserving benign intent and avoiding new unsafe content. This directly addresses the load-bearing assumption. revision: yes
Referee: [Experiments] The experimental section provides no information on dataset construction, choice of evaluation metrics for helpfulness/naturalness/security, statistical significance testing, or controls for prompt difficulty and baseline prompt engineering. Without these, the reported outperformance cannot be verified or reproduced.

Authors: We agree that these details are necessary for reproducibility and verification. The revised experimental section will include: a complete account of dataset construction (sources, filtering, sizes, and any annotation procedures); explicit definitions, computation methods, and justifications for the helpfulness, naturalness, and security metrics; statistical significance testing (e.g., paired t-tests or bootstrap confidence intervals) on the reported improvements; and controls such as stratification by prompt difficulty/risk level plus comparisons against prompt-engineered baselines. These additions will allow independent verification of the outperformance claims. revision: yes

Circularity Check

0 steps flagged

No circularity: method uses standard variational inference without self-referential reduction

full rationale

The abstract describes LANCE as applying variational inference to generate continuous rejection distributions from label enhancement, which then supply gradients to a refinement model. No equations, loss functions, or derivations are shown that equate the output distributions or experimental gains to the inputs by construction. No self-citations, uniqueness theorems, or ansatzes are invoked as load-bearing premises. The performance claims rest on empirical experiments rather than logical equivalence to fitted parameters or prior author results. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that rejection can be usefully modeled as a continuous distribution over discrete categories and that variational inference can recover this distribution from available safety data. No explicit free parameters or invented entities are named in the abstract.

axioms (1)

domain assumption Rejection behavior in LLMs can be represented as a continuous distribution over multiple categories rather than a single binary or template-based refusal.
Invoked in the description of label enhancement via variational inference.

pith-pipeline@v0.9.0 · 5471 in / 1201 out tokens · 26749 ms · 2026-05-11T01:50:19.450113+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 5 internal anchors

[1]

Prompt optimization and evalu- ation for llm automated red teaming.arXiv preprint arXiv:2507.22133,

Freenor, M., Alvarez, L., Leal, M., Smith, L., Garrett, J., Husieva, Y ., Woodruff, M., Miller, R., Kummerfeld, E., Medeiros, R., et al. Prompt optimization and evalu- ation for llm automated red teaming.arXiv preprint arXiv:2507.22133,

work page arXiv
[2]

Evaluating large language models: A comprehensive survey

Guo, Z., Jin, R., Liu, C., Huang, Y ., Shi, D., Yu, L., Liu, Y ., Li, J., Xiong, B., Xiong, D., et al. Evaluating large lan- guage models: A comprehensive survey.arXiv preprint arXiv:2310.19736,

work page arXiv
[3]

Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Inan, H., Upasani, K., Chi, J., Rungta, R., Iyer, K., Mao, Y ., Tontchev, M., Hu, Q., Fuller, B., Testug- gine, D., et al. Llama guard: Llm-based input-output 9 Beyond “I cannot fulfill this request”: Alleviating Rigid Rejection in LLMs via Label Enhancement safeguard for human-ai conversations.arXiv preprint arXiv:2312.06674,

work page internal anchor Pith review Pith/arXiv arXiv
[4]

Learning to extract con- text for context-aware llm inference.arXiv preprint arXiv:2512.11986,

Kim, M., Caccia, L., Shi, Z., Pereira, M., Côté, M.-A., Yuan, X., and Sordoni, A. Learning to extract con- text for context-aware llm inference.arXiv preprint arXiv:2512.11986,

work page arXiv
[5]

P., Hermann, K., Welleck, S., Yazdanbakhsh, A., and Clark, P

Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y ., Gupta, S., Majumder, B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., and Clark, P. Self-refine: Iterative refinement with self-feedback. InAdvances in Neural In- formation Processing Systems 36: Annual Conference on Neural Informatio...

work page 2023
[6]

Rimsky, N., Gabrieli, N., Schulz, J., Tong, M., Hubinger, E., and Turner, A. M. Steering llama 2 via contrastive activation addition. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V olume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024, pp. 15504–15522,

work page 2024
[7]

XSTest: A test suite for identifying exaggerated safety behaviours in large language mod- els

Röttger, P., Kirk, H., Vidgen, B., Attanasio, G., Bianchi, F., and Hovy, D. XSTest: A test suite for identifying exaggerated safety behaviours in large language mod- els. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5377– 5400,

work page 2024
[8]

Team, Q. et al. Qwen2 technical report.arXiv preprint arXiv:2407.10671, 2(3),

work page internal anchor Pith review Pith/arXiv arXiv
[9]

Llama 2: Open Foundation and Fine-Tuned Chat Models

Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y ., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. Llama 2: Open foundation and fine- tuned chat models.arXiv preprint arXiv:2307.09288,

work page internal anchor Pith review Pith/arXiv arXiv
[10]

One positive label is sufficient: Single-positive multi-label learning with label enhancement

Xu, N., Qiao, C., Lv, J., Geng, X., and Zhang, M. One positive label is sufficient: Single-positive multi-label learning with label enhancement. InAdvances in Neu- ral Information Processing Systems 35: Annual Confer- ence on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022,

work page 2022
[11]

J., Prakash, N., Neo, C., Lee, R

Yeo, W. J., Prakash, N., Neo, C., Lee, R. K.-W., Cam- bria, E., and Satapathy, R. Understanding refusal in lan- guage models with sparse autoencoders.arXiv preprint arXiv:2505.23556,

work page arXiv
[12]

TextGrad: Automatic "Differentiation" via Text

Yuksekgonul, M., Bianchi, F., Boen, J., Liu, S., Huang, Z., Guestrin, C., and Zou, J. Textgrad: Automatic "dif- ferentiation" via text.arXiv preprint arXiv:2406.07496,

work page internal anchor Pith review arXiv
[13]

Representation Engineering: A Top-Down Approach to AI Transparency

Zhang, Z., Lei, L., Wu, L., Sun, R., Huang, Y ., Long, C., Liu, X., Lei, X., Tang, J., and Huang, M. SafetyBench: Evaluating the safety of large language models. InPro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V olume 1: Long Papers), pp. 15537–15553. Association for Computational Lin- guistics, August 2024a. 1...

work page internal anchor Pith review Pith/arXiv arXiv 2024