Multilingual Large Language Models considerably changed how technologies influence language.While previous technologies could mediate or assist humans, there is now a tendency to offload the task of writing itself to these technologies, enabling models to change our languages more directly.While they provide us quick access to information and impressively fluent output, beneath their (apparent) sophistication lies a subtle, insidious threat: the gradual decline and loss of linguistic diversity.In this position paper, I explore how model collapse, with a particular focus on translation technology, can lead to the loss of linguistic forms, grammatical features, and cultural nuance.Model collapse refers to the consequences of self-consuming training loops, where automatically generated data (re-)enters the training data, leading to a gradual distortion of the data distribution and the underrepresentation of low-probability linguistic phenomena.Drawing on recent work in Computer Vision, Natural Language Processing and Machine Translation, I argue that the many tails of our linguistic distributions might be vanishing, and with them, the narratives and identities they carry.This paper is a call to resist linguistic flattening and to reimagine Natural Language Processing as a field that encourages, values and protects expressive multilingual diversity and creativity.

Generative AI tools, such as ChatGPT, are applied to a wide range of languagerelated tasks, including translation. Despite their current popularity among users and researchers and the impressive results obtained on several benchmarks (Kocmi et al., 2024a; Deutsch et al., 2025), their potential side-effects on languages and translations are still understudied (Vanmassenhove, 2025). The paradigm shift from Machine Translation (MT) to Generative AI Translation (GAIT) likely calls for a reconsideration of our assessment and evaluation metrics and practices. In this work, we focus on GAIT by analyzing translations from four multilingual large language models (MLLMs), mBART, Jamba-1.5-large, GPT 4o and DeepSeek R1 applied to three different domains (news, literature and poetry) for the English-Dutch language pair. Focusing on metrics related to lexical and textual diversity, we find that while GAIT text for literature if of significantly high lexical and grammatical richness, that is not the case for news and poetry. We also assess the homogeneity of AI-generated text through a set of clustering and classification experiments. In addition to a clear separation between human- and AI-generated content, our results indicate that GAIT output is more homogeneous among MLLMs.

While Large Language Models achieve state-of-the-art results across a wide range of NLP tasks, they remain prone to systematic biases. Among these, gender bias is particularly salient in MT, due to systematic differences across languages in whether and how gender is marked. As a result, translation often requires disambiguating implicit source signals into explicit gender-marked forms. In this context, standard benchmarks may capture broad disparities but fail to reflect the full complexity of gender bias in modern MT. In this paper, we extend recent frameworks on bias evaluation by: (i) introducing a novel measure coined ’Prior Bias’, capturing a model’s default gender assumptions, and (ii) applying the framework to decoder-only MT models. Our results show that, despite their scale and state-of-the-art status, decoder-only models do not generally outperform encoder-decoder architectures on gender-specific metrics; however, post-training (e.g., instruction tuning) not only improves contextual awareness but also reduces the masculine Prior Bias.

The purpose of this research is to demonstrate the feasibility of automating ‘die-polygon-capturing’, an economical yet still labor-intensive technique for circuit extraction during the reverse-engineering of simple integrated circuits. As microchip designs become increasingly diverse with the ongoing trend of using application-specific integrated circuits, and considering the importance of reverse-engineering these components, die-polygon-capturing is set to play a more critical role in the future. Due to the apparent absence of prior scientific publications and limited automation efforts in this area, this paper presents a proof of concept for automating the die-polygon-capturing technique, thereby addressing a notable gap in the existing literature, with an overarching goal of reducing the labor-intensity of die-polygon-capturing. Our method consists of training deep neural networks on variations of a dataset and evaluating their segmentation capabilities of various layers and connections in an integrated circuit. Given the limited accessibility to high-quality labeled datasets, the dataset used for this research consists of two images of a single microchip, the AMD 9085D. We implemented a data augmentation process that expanded this dataset to as many as 4872 images. The experiment’s results proof the automation’s feasibility, demonstrating high scores on Intersection over Union and F-beta evaluation metrics. However the primary conclusion drawn is the need for focus on generalizability towards other types of microchips in order to effectively automate this technique for circuit extraction. • This research automates the die-polygon-capturing technique as a proof of concept. • Automating circuit layers’ semantic segmentation substantially reduces manual labor. • Each feature-set variant offers unique advantages for predicting specific labels. • Augmentation positioning influences the quality of segmentation across different layers. • Future improvements in generalization can scale automated die-polygon-capturing.

While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled data is often lacking due to the high-cost and effort associated with labeling such data. Aside from the data scarcity challenge, QE models should also be generalizabile, i.e., they should be able to handle data from different domains, both generic and specific. To alleviate these two main issues — data scarcity and domain mismatch — this paper combines domain adaptation and data augmentation within a robust QE system. Our method is to first train a generic QE model and then fine-tune it on a specific domain while retaining generic knowledge. Our results show a significant improvement for all the language pairs investigated, better cross-lingual inference, and a superior performance in zero-shot learning scenarios as compared to state-of-the-art baselines.

Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data.The amplification of biases in language technology has mainly been examined with respect to specific phenomena, such as gender bias.In this work, we go beyond the study of gender in MT and investigate how bias amplification might affect language in a broader sense.We hypothesize that the 'algorithmic bias', i.e. an exacerbation of frequently observed patterns in combination with a loss of less frequent ones, not only exacerbates societal biases present in current datasets but could also lead to an artificially impoverished language: 'machine translationese'.We assess the linguistic richness (on a lexical and morphological level) of translations created by different data-driven MT paradigms -phrase-based statistical (PB-SMT) and neural MT (NMT).Our experiments show that there is a loss of lexical and morphological richness in the translations produced by all investigated MT paradigms for two language pairs (EN↔FR and EN↔ES).

Recent years have seen an increasing need for gender-neutral and inclusive language.Within the field of NLP, there are various mono-and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents.In this work, we present a rulebased and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks.A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.

Research on speaker-adapted neural machine translation (NMT) is scarce. One of the main challenges for more personalized MT systems is finding large enough annotated parallel datasets with speaker information. Rabinovich et al. (2017) published an annotated parallel dataset for EN–FR and EN–DE, however, for many other language pairs no sufficiently large annotated datasets are available.

Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921;Slobin, 1996).One such difference is related to the way gender is expressed in a language.Saying "I am happy" in English, does not encode any additional knowledge of the speaker that uttered the sentence.However, many other languages do have grammatical gender systems and so such knowledge would be encoded.In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained/recovered.The same sentence would become either "Je suis heureux", for a male speaker or "Je suis heureuse" for a female one.Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or even on the level of syntactic constructions (Tannen, 1991;Pennebaker et al., 2003).We integrate gender information into NMT systems.Our contribution is twofold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs.Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs.