Building language technologies and conducting NLP research for low-resource languages---particularly when led by native speakers or involving participatory research practices---are often framed as mea...
arXiv:2405.06818v2 Announce Type: replace Abstract: Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward...
arXiv:2506. 17467v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society.
arXiv:2609.22494v1 Announce Type: new Abstract: In recent years, there has been a surge of interest in Cultural NLP, with substantial efforts to create globally inclusive NLP systems. The rapid growt...
The paper examines how four stakeholder groups—AI developers, professional translators, language learners, and language service providers—discuss machine translation on social media. Using a dataset of 79,286 posts from Reddit, Facebook, Bluesky, and Mastodon (2019‑2025), the authors find frequent disagreements and strong conflicts over translation quality, efficiency, and reliability. These conflicts arise because AI communities view the issues as technical, while non‑AI users prioritize quality nuances, time savings, trust, and broader social concerns.
The paper investigates language-of-study (LoS) bias in NLP peer reviews, defining and distinguishing negative and positive forms of bias. Using a new dataset, LOBSTER, and an LLM-based detection pipeline, the authors analyze 15,645 reviews and find that non‑English papers experience significantly higher bias rates, with negative bias outweighing positive bias. They further identify four subcategories of negative bias, noting that demanding unjustified cross‑lingual generalization is the most common.
Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more.
arXiv:2606. 13397v1 Announce Type: cross Abstract: Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online.
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.
arXiv:2607. 06544v1 Announce Type: new Abstract: As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization.
The paper explores how "culture" can be operationalised in Natural Language Processing (NLP) and what this reveals about the possibilities and limits of considering a plurality of cultural backgrounds in technological design. It proposes that cultural alignment cannot be achieved only by adding more examples of "other cultures", rather it requires plural epistemologies: allowing multiple, locally grounded ways of knowing.
arXiv:2609.01432v1 Announce Type: cross Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentionin...