Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding

Liesbeth Allein; Maria Mihaela Trusca

arxiv: 2503.08159 · v1 · submitted 2025-03-11 · 💻 cs.CL

Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding

Maria Mihaela Trusca , Liesbeth Allein This is my paper

Pith reviewed 2026-05-23 00:51 UTC · model grok-4.3

classification 💻 cs.CL

keywords toxicity decodingout-of-context sentencescontrolled generationhuman interpretation alignmentlanguage model decodinginterpretation diversitysemantic similarityprediction uncertainty

0 comments

The pith

A decoding strategy that controls toxicity in generated text produces interpretations of out-of-context sentences that align better with human syntax and semantics.

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

The paper introduces a decoding approach that steers the toxicity levels of language-model outputs to simulate the range of ways people might read a sentence when surrounding context is missing. It does so through three explicit controls: matching the toxicity of each interpretation to the input sentence, easing the toxicity limit when the input itself is more toxic, and encouraging a spread of toxicity values across the set of outputs. The authors report that these controls yield generations closer to human-written interpretations on both syntactic and semantic measures and lower the model's uncertainty about its predictions. If the controls work as intended, models could surface potential misreadings and hidden toxic implications before they reach users.

Core claim

Our proposed decoding strategy explicitly controls toxicity in the set of generated interpretations by (i) aligning interpretation toxicity with the input, (ii) relaxing toxicity constraints for more toxic input sentences, and (iii) promoting diversity in toxicity levels within the set of generated interpretations. Experimental results show that our method improves alignment with human-written interpretations in both syntax and semantics while reducing model prediction uncertainty.

What carries the argument

The controlled toxicity decoding strategy that aligns toxicity to the input, relaxes constraints for toxic inputs, and promotes diversity across outputs.

If this is right

Generated interpretations match human syntax and semantics more closely than uncontrolled baselines.
Model prediction uncertainty decreases when toxicity is explicitly managed.
Hidden toxic meanings in ambiguous sentences become more visible through the diversified outputs.
Anticipation of reader misunderstandings improves by producing sets of interpretations rather than single outputs.

Where Pith is reading between the lines

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

The same alignment-relax-diversify pattern could be applied to other scalar attributes such as sentiment polarity or factual specificity.
Systems that generate multiple controlled interpretations might be used upstream of content moderation to flag sentences that admit harmful readings.
The approach may require language-specific toxicity classifiers to maintain performance outside English.

Load-bearing premise

Toxicity can be reliably quantified and steered during decoding to reproduce the range of human interpretations of out-of-context text.

What would settle it

A side-by-side human evaluation in which the new decoding method produces no measurable gain in syntactic or semantic match to human interpretations and no reduction in prediction uncertainty compared with standard sampling.

read the original abstract

Interpretations of a single sentence can vary, particularly when its context is lost. This paper aims to simulate how readers perceive content with varying toxicity levels by generating diverse interpretations of out-of-context sentences. By modeling toxicity, we can anticipate misunderstandings and reveal hidden toxic meanings. Our proposed decoding strategy explicitly controls toxicity in the set of generated interpretations by (i) aligning interpretation toxicity with the input, (ii) relaxing toxicity constraints for more toxic input sentences, and (iii) promoting diversity in toxicity levels within the set of generated interpretations. Experimental results show that our method improves alignment with human-written interpretations in both syntax and semantics while reducing model prediction uncertainty.

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 offers a three-part toxicity control strategy in decoding to generate varied interpretations of out-of-context sentences, but the abstract provides no experimental details to back the claimed gains.

read the letter

The main thing to know is that this work describes a decoding method that steers toxicity in generated interpretations of sentences stripped of context. It does this by aligning output toxicity to the input, relaxing constraints on toxic inputs, and pushing for diversity across the set of interpretations. The authors claim this produces outputs closer to human-written ones in syntax and semantics while lowering model uncertainty. The three controls are a straightforward way to operationalize the goal of capturing human variability in how people read ambiguous text, especially around toxicity levels. That focus on a practical issue in content safety and online communication is reasonable. The method targets sets of interpretations rather than single outputs, which matches the stated aim. The soft spots are clear from the abstract alone. No datasets, baselines, metrics, or statistical tests are mentioned, so there is no way to judge whether the reported improvements are real or meaningful. The central modeling choice—that toxicity can be quantified and steered during decoding to match human interpretation ranges—rests on unshown implementation details like the toxicity scorer and how the three mechanisms are actually implemented. Without the full experimental section it is hard to tell if the results hold up or if they depend on particular choices that might not generalize. This would interest researchers working on controllable text generation or toxicity handling in NLP. A reader looking for new decoding tricks might find the idea worth checking once the missing details are supplied. I would send it for peer review because the problem is relevant and the approach is concrete enough that referees could usefully push on the evaluation and novelty relative to prior decoding work.

Referee Report

2 major / 0 minor

Summary. The paper proposes a controlled toxicity decoding strategy for generating sets of interpretations of out-of-context sentences. The strategy has three explicit mechanisms: (i) aligning the toxicity of interpretations with that of the input sentence, (ii) relaxing toxicity constraints when the input is more toxic, and (iii) promoting diversity in toxicity levels across the generated set. The central empirical claim is that this produces interpretations that align better with human-written ones in both syntax and semantics while also reducing model prediction uncertainty.

Significance. If the experimental results hold under rigorous evaluation, the work would be moderately significant for the subfield of controlled decoding and toxicity-aware generation. It offers a concrete, mechanism-driven approach to modeling human interpretive variability rather than relying solely on standard sampling or beam search. The absence of any parameter-free derivation or machine-checked component means the contribution is entirely empirical; its value therefore hinges entirely on the quality and transparency of the reported experiments.

major comments (2)

[Abstract] Abstract (and presumably §4): the central claim of improved syntactic/semantic alignment and reduced uncertainty is stated without any description of the datasets, baselines, evaluation metrics, or statistical tests. This information is load-bearing for the empirical contribution and must be supplied before the claim can be assessed.
[Abstract] The three control mechanisms rest on the assumption that toxicity scores can be reliably estimated and steered at decoding time to reproduce the distribution of human interpretations. No concrete validation of this modeling assumption (e.g., correlation between steered toxicity and human judgment variance) is referenced in the provided text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below and will revise the manuscript accordingly to improve transparency and completeness of the empirical claims.

read point-by-point responses

Referee: [Abstract] Abstract (and presumably §4): the central claim of improved syntactic/semantic alignment and reduced uncertainty is stated without any description of the datasets, baselines, evaluation metrics, or statistical tests. This information is load-bearing for the empirical contribution and must be supplied before the claim can be assessed.

Authors: We agree that the abstract would be strengthened by briefly contextualizing the experimental setup so that the central claims can be assessed at a glance. In the revised version we will expand the abstract with a concise description of the datasets (collections of out-of-context sentences paired with human interpretations), the baselines (standard sampling and beam search), the evaluation metrics (syntactic similarity via constituency parse metrics and semantic alignment via sentence embeddings), and the statistical tests used (paired t-tests with reported p-values). Full experimental details will remain in §4; the abstract addition will be limited to one or two sentences. revision: yes
Referee: [Abstract] The three control mechanisms rest on the assumption that toxicity scores can be reliably estimated and steered at decoding time to reproduce the distribution of human interpretations. No concrete validation of this modeling assumption (e.g., correlation between steered toxicity and human judgment variance) is referenced in the provided text.

Authors: The empirical results in §4 demonstrate that the controlled toxicity outputs align more closely with human interpretations than baselines, which provides indirect support for the assumption. However, we acknowledge that an explicit statement or analysis of the correlation between steered toxicity and observed human variance is not currently referenced. We will therefore add a short paragraph (or footnote) in the methodology section that reports the relevant correlation coefficient computed from our human-annotated data, thereby making the modeling assumption more transparent. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical decoding method with no self-referential derivations

full rationale

The paper describes a controlled decoding strategy for generating interpretations with modulated toxicity levels, presented as an empirical technique evaluated against human-written interpretations. No equations, fitted parameters, or derivation chains are visible in the provided abstract or description. The three control mechanisms (alignment, relaxation, diversity) are modeling choices whose outcomes are assessed experimentally rather than derived by construction from inputs. No self-citations, uniqueness theorems, or ansatzes are referenced as load-bearing. The central claims rest on experimental results (improved alignment, reduced uncertainty) rather than any reduction to the method's own definitions. This is the expected non-finding for a purely empirical NLP decoding paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities are identifiable from the provided text.

pith-pipeline@v0.9.0 · 5633 in / 987 out tokens · 40810 ms · 2026-05-23T00:51:41.176028+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 1 internal anchor

[1]

In Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024, pages 116–122, Torino, Italia

OrigamIM: A dataset of ambiguous sentence interpre- tations for social grounding and implicit language un- derstanding. In Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024, pages 116–122, Torino, Italia. ELRA and ICCL. Liesbeth Allein, Maria Mihaela Trusca, and Marie- Francine Moens

work page 2024
[2]

METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the Workshop on Intrinsic and Extrinsic Evalua- tion Measures for Machine Translation and/or Sum- marization@ACL 2005, Ann Arbor, Michigan, USA, June 29, 2005, pages 65–72. Association for Compu- tational Linguistics. Sumanth Dathathri, Andrea M...

work page 2005
[3]

In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173–8188, Online

Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173–8188, Online. As- sociation for Computational Linguistics. Mai ElSherief, Caleb Ziems, David Muchlinski, Vaish- navi Anupindi, Jordyn Seybolt, Munmun De Choud- hury, and Diyi Yang

work page 2020
[4]

In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 345–363, Online and Punta Cana, Dominican Republic

Latent hatred: A bench- mark for understanding implicit hate speech. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 345–363, Online and Punta Cana, Dominican Republic. Asso- ciation for Computational Linguistics. Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith

work page 2021
[5]

In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3356–3369, Online

RealToxi- cityPrompts: Evaluating neural toxic degeneration in language models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3356–3369, Online. Association for Computational Linguistics. Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar

work page 2020
[6]

In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,

The curious case of neural text degeneration. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,

work page 2020
[7]

In Findings of the Association for Computational Linguistics: ACL 2023, pages 4598–4612, Toronto, Canada

Critic-guided decoding for controlled text generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4598–4612, Toronto, Canada. Association for Computational Linguistics. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer

work page 2023
[8]

In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 7871–7880

BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and com- prehension. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 7871–7880. Association for Computational Linguistics. Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S...

work page 2020
[9]

In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9194–9206, Online

Like hiking? you probably enjoy nature: Persona- grounded dialog with commonsense expansions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9194–9206, Online. Association for Computa- tional Linguistics. Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukher- jee

work page 2020
[10]

Hatexplain: A benchmark dataset for ex- plainable hate speech detection. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial In- telligence, EAAI 2021, Virtual Event, February 2-9, 2021, ...

work page 2021
[11]

In Find- ings of the Association for Computational Linguis- tics: EMNLP 2021, pages 3973–3997, Punta Cana, Dominican Republic

A plug-and- play method for controlled text generation. In Find- ings of the Association for Computational Linguis- tics: EMNLP 2021, pages 3973–3997, Punta Cana, Dominican Republic. Association for Computational Linguistics. Shrimai Prabhumoye, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro

work page 2021
[12]

In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Process- ing, EMNLP 2020, Online, November 16-20, 2020, pages 2685–2702

COMET: A neural framework for MT evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Process- ing, EMNLP 2020, Online, November 16-20, 2020, pages 2685–2702. Association for Computational Linguistics. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozièr...

work page 2020
[13]

LLaMA: Open and Efficient Foundation Language Models

Llama: Open and efficient foundation language models. CoRR, abs/2302.13971. David Wingate, Mohammad Shoeybi, and Taylor Sorensen

work page internal anchor Pith review Pith/arXiv arXiv
[14]

In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5621–5634, Abu Dhabi, United Arab Emirates

Prompt compression and contrastive conditioning for controllability and toxicity reduction in language models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5621–5634, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics. Kevin Yang and Dan Klein

work page 2022
[15]

In Pro- ceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3511–3535, Online

FUDGE: Controlled text generation with future discriminators. In Pro- ceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3511–3535, Online. Association for Computational Linguistics. Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing C...

work page 2021

[1] [1]

In Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024, pages 116–122, Torino, Italia

OrigamIM: A dataset of ambiguous sentence interpre- tations for social grounding and implicit language un- derstanding. In Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024, pages 116–122, Torino, Italia. ELRA and ICCL. Liesbeth Allein, Maria Mihaela Trusca, and Marie- Francine Moens

work page 2024

[2] [2]

METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the Workshop on Intrinsic and Extrinsic Evalua- tion Measures for Machine Translation and/or Sum- marization@ACL 2005, Ann Arbor, Michigan, USA, June 29, 2005, pages 65–72. Association for Compu- tational Linguistics. Sumanth Dathathri, Andrea M...

work page 2005

[3] [3]

In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173–8188, Online

Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173–8188, Online. As- sociation for Computational Linguistics. Mai ElSherief, Caleb Ziems, David Muchlinski, Vaish- navi Anupindi, Jordyn Seybolt, Munmun De Choud- hury, and Diyi Yang

work page 2020

[4] [4]

In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 345–363, Online and Punta Cana, Dominican Republic

Latent hatred: A bench- mark for understanding implicit hate speech. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 345–363, Online and Punta Cana, Dominican Republic. Asso- ciation for Computational Linguistics. Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith

work page 2021

[5] [5]

In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3356–3369, Online

RealToxi- cityPrompts: Evaluating neural toxic degeneration in language models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3356–3369, Online. Association for Computational Linguistics. Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar

work page 2020

[6] [6]

In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,

The curious case of neural text degeneration. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,

work page 2020

[7] [7]

In Findings of the Association for Computational Linguistics: ACL 2023, pages 4598–4612, Toronto, Canada

Critic-guided decoding for controlled text generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4598–4612, Toronto, Canada. Association for Computational Linguistics. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer

work page 2023

[8] [8]

In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 7871–7880

BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and com- prehension. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 7871–7880. Association for Computational Linguistics. Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S...

work page 2020

[9] [9]

In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9194–9206, Online

Like hiking? you probably enjoy nature: Persona- grounded dialog with commonsense expansions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9194–9206, Online. Association for Computa- tional Linguistics. Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukher- jee

work page 2020

[10] [10]

Hatexplain: A benchmark dataset for ex- plainable hate speech detection. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial In- telligence, EAAI 2021, Virtual Event, February 2-9, 2021, ...

work page 2021

[11] [11]

In Find- ings of the Association for Computational Linguis- tics: EMNLP 2021, pages 3973–3997, Punta Cana, Dominican Republic

A plug-and- play method for controlled text generation. In Find- ings of the Association for Computational Linguis- tics: EMNLP 2021, pages 3973–3997, Punta Cana, Dominican Republic. Association for Computational Linguistics. Shrimai Prabhumoye, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro

work page 2021

[12] [12]

In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Process- ing, EMNLP 2020, Online, November 16-20, 2020, pages 2685–2702

COMET: A neural framework for MT evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Process- ing, EMNLP 2020, Online, November 16-20, 2020, pages 2685–2702. Association for Computational Linguistics. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozièr...

work page 2020

[13] [13]

LLaMA: Open and Efficient Foundation Language Models

Llama: Open and efficient foundation language models. CoRR, abs/2302.13971. David Wingate, Mohammad Shoeybi, and Taylor Sorensen

work page internal anchor Pith review Pith/arXiv arXiv

[14] [14]

In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5621–5634, Abu Dhabi, United Arab Emirates

Prompt compression and contrastive conditioning for controllability and toxicity reduction in language models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5621–5634, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics. Kevin Yang and Dan Klein

work page 2022

[15] [15]

In Pro- ceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3511–3535, Online

FUDGE: Controlled text generation with future discriminators. In Pro- ceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3511–3535, Online. Association for Computational Linguistics. Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing C...

work page 2021