Inference-Time Toxicity Mitigation in Protein Language Models
Authors:
Manuel Fernández Burda,
Santiago Aranguri,
Iván Arcuschin Moreno,
Enzo Ferrante
Abstract:
Protein language models (PLMs) are becoming practical tools for de novo protein design, yet their dual-use potential raises safety concerns. We show that domain adaptation to specific taxonomic groups can elicit toxic protein generation, even when toxicity is not the training objective. To address this, we adapt Logit Diff Amplification (LDA) as an inference-time control mechanism for PLMs. LDA mo…
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Protein language models (PLMs) are becoming practical tools for de novo protein design, yet their dual-use potential raises safety concerns. We show that domain adaptation to specific taxonomic groups can elicit toxic protein generation, even when toxicity is not the training objective. To address this, we adapt Logit Diff Amplification (LDA) as an inference-time control mechanism for PLMs. LDA modifies token probabilities by amplifying the logit difference between a baseline model and a toxicity-finetuned model, requiring no retraining. Across four taxonomic groups, LDA consistently reduces predicted toxicity rate (measured via ToxDL2) below the taxon-finetuned baseline while preserving biological plausibility. We evaluate quality using Fréchet ESM Distance and predicted foldability (pLDDT), finding that LDA maintains distributional similarity to natural proteins and structural viability (unlike activation-based steering methods that tend to degrade sequence properties). Our results demonstrate that LDA provides a practical safety knob for protein generators that mitigates elicited toxicity while retaining generative quality.
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Submitted 4 March, 2026;
originally announced March 2026.
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
Authors:
Israfel Salazar,
Manuel Fernández Burda,
Shayekh Bin Islam,
Arshia Soltani Moakhar,
Shivalika Singh,
Fabian Farestam,
Angelika Romanou,
Danylo Boiko,
Dipika Khullar,
Mike Zhang,
Dominik Krzemiński,
Jekaterina Novikova,
Luísa Shimabucoro,
Joseph Marvin Imperial,
Rishabh Maheshwary,
Sharad Duwal,
Alfonso Amayuelas,
Swati Rajwal,
Jebish Purbey,
Ahmed Ruby,
Nicholas Popovič,
Marek Suppa,
Azmine Toushik Wasi,
Ram Mohan Rao Kadiyala,
Olga Tsymboi
, et al. (20 additional authors not shown)
Abstract:
The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and languages, many rely on translations of English datasets, failing to capture cultural nuances. In this work, we propose Kaleidoscope, as the most comprehensive exam b…
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The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and languages, many rely on translations of English datasets, failing to capture cultural nuances. In this work, we propose Kaleidoscope, as the most comprehensive exam benchmark to date for the multilingual evaluation of vision-language models. Kaleidoscope is a large-scale, in-language multimodal benchmark designed to evaluate VLMs across diverse languages and visual inputs. Kaleidoscope covers 18 languages and 14 different subjects, amounting to a total of 20,911 multiple-choice questions. Built through an open science collaboration with a diverse group of researchers worldwide, Kaleidoscope ensures linguistic and cultural authenticity. We evaluate top-performing multilingual vision-language models and find that they perform poorly on low-resource languages and in complex multimodal scenarios. Our results highlight the need for progress on culturally inclusive multimodal evaluation frameworks.
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Submitted 29 April, 2025; v1 submitted 9 April, 2025;
originally announced April 2025.