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Adapting large language models (LLMs) to extremely low-resource languages remains challenging due to severe data scarcity and the lack of structured linguistic supervision. We introduce GCCLA, a graph-conditioned cross-lingual adaptation framework that integrates multilingual knowledge graphs into parameter-efficient LLM adaptation. GCCLA conditions a frozen multilingual LLM on structured semantic and typological relations encoded in a multilingual graph, providing a strong inductive bias for data-efficient transfer. We instantiate and evaluate the framework through a focused case study on English-to-Amharic-to-Tigrinya transfer, where labeled data is extremely limited. By separating knowledge representation from language modeling, GCCLA stabilizes learning and improves sample efficiency in few-shot regimes. We evaluate the approach on five tasks, sentiment analysis, named entity recognition, natural language inference, question answering, and extractive summarization, under extreme data scarcity, with as few as 0–1000 labeled Tigrinya examples. Experimental results show that GCCLA consistently outperforms multilingual, translation-based, and parameter-efficient baselines, achieves competitive performance with as few as 100 labeled examples, and degrades gracefully under partial graph coverage. These findings demonstrate that graph conditioning is an effective principle for data-efficient cross-lingual adaptation of LLMs advancing equitable NLP.

ACL 2026

GCCLA: Graph-Conditioned Cross-Lingual Adaptation of Large Language Models Under Extreme Data Scarcity (A Case Study in Tigrigna)

graph-conditioned learning

low-resource languages nlp

parameter-efficient adaptation

language typology

knowledge graphs

few-shot learning

cross-lingual transfer

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

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The global use of artificial intelligence for content creation has led to an interest in assessing the ability to generate culturally representative content. We explore whether generated content is localized through cultural markers that are (a) inserted within a generic narrative template or (b) distributed throughout to create culturally-salient narratives, plots, and themes. We analyze stories generated by five models on 125 topics for 193 nationalities. We find that only a small subset (9-17%) of the vocabulary accounts for the variation in stories across nationalities. After removal of these cultural markers, we find that the remaining narrative is homogeneous across cultural contexts. Moreover, we find varying degrees of stereotypicality in cultural markers. Finally, markers from 19 countries, mostly located in the Global South where dominant languages are lower resourced, are on average offensive, while those from the rest are on average non-offensive or neutral.

Cultural Markers and Narrative Homogeneity in AI-Generated Stories

Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, underserving primarily oral languages and indigenous communities. This position paper uses Guaraní, an official and widely spoken language of Paraguay, as a case study to argue that language support in AI remains insufficient unless it aligns with lived oral practices. We propose an alternative to the standard "text-to-speech'' pipeline, proposing instead an oral-first multi-agent architecture. The architecture is intended to illustrate the design implications of our cultural argument rather than to propose a deployable technical solution. By decoupling Guaraní natural language understanding from dedicated agents for conversation state and community-led governance, we sketch a technical framework that respects indigenous data sovereignty and diglossia. Our work moves beyond mere recognition to focus on turn-taking, repair, and shared context as the primary locus of interaction. We conclude that for AI to be truly culturally grounded, it must shift from adapting oral languages to text-centric systems to treating spoken conversation as a first-class design requirement, ensuring digital ecosystems empower rather than overlook diverse linguistic practices.

Let’s Talk, Not Type: An Oral-First Multi-Agent Architecture for Guaraní

We evaluate whether open-source LLMs can produce proficiency-graded English adaptations of entries from the \textit{Diccionario de colombianismos} (DiCol), a Colombian Spanish lexicographic resource used in language teaching. Three 7--8B instruction-tuned models---Llama~3.1, Qwen2.5, and Mistral---generate Beginner, Intermediate, and Advanced translations for all 8{,}252 definitions using structured zero-shot prompts identical across levels except for the target CEFR band. Automated metrics show that Intermediate targeting collapses (73--83\% classified as Advanced by vocabulary, $\chi^2 > 705$, $p < .001$) and that Advanced outputs expand 4.9--8.2$\times$ relative to the source. Expert annotation of a 360-entry stratified sample ($\kappa = 0.61$--$0.68$) identifies hallucination in 19\% of entries (Fleiss' $\kappa = 0.77$ for cultural preservation categories, 97\% unanimity among flagged cases). Hallucination concentrates in the Advanced condition (81\%, $\chi^2 = 86.6$, $p < .001$) and is associated with higher expansion ($U = 16{,}662$, $p < .001$, $r = 0.68$), manifesting primarily as generic elaboration and, in a smaller proportion, as Colombia-stereotyping and pragmatic polarity inversion. We discuss these findings through the lens of \citeauthor{venuti1995invisibility}'s domestication framework and describe the observed pattern as \emph{algorithmic domestication}.

LLM-Adapted Colombian Spanish Lexicography: Proficiency Control, Hallucination, and Cultural Distortion

Large Language Models (LLMs) show unbalanced knowledge of cultures across the globe, favoring high-resource cultures over low-resource ones. A possible way to tackle this issue is to fine-tune LLMs on culturally specific data. However, fine-tuning recent LLMs requires high computational resources as well as memory storage, which triggered the development of parameter-efficient fine-tuning (PEFT) approaches, the most widespread being LoRA. In this article, we investigate the use of another class of PEFT approaches, namely soft prompt methods (prompt-tuning and prefix-tuning), to improve LLMs' cultural knowledge across diverse cultures. We focus on cultural alignment on Multiple-Choice Questions of cultural commonsense knowledge. On this task with limited fine-tuning data, we show that soft-prompt-based methods outperform LoRA in comparable settings. Moreover, the trained soft prompts are interpretable and capture similarities between cultures.

Soft Prompts for Adapting LLMs to Cultural Commonsense Knowledge

Culture shapes the objects people use and for what purposes, yet mainstream Vision-Language (VL) datasets frequently exhibit cultural biases, disproportionately favoring higher-income, Western contexts. This imbalance reduces model generalizability and perpetuates performance disparities, especially impacting lower-income and non-Western communities. To address these disparities, we propose a novel function-centric framework that categorizes objects by the functions they fulfill, across diverse cultural and economic contexts. We implement this framework by creating the Culture Affordance Atlas, a re-annotated and culturally grounded restructuring of the Dollar Street dataset spanning 46 functions and 288 objects. Through extensive empirical analyses using the CLIP model, we demonstrate that function-centric labels substantially reduce socioeconomic performance gaps between high- and low-income groups by a median of 6 pp (statistically significant), improving model effectiveness for lower-income contexts. Furthermore, our analyses reveals numerous culturally essential objects that are frequently overlooked in prominent VL datasets. Our contributions offer a scalable pathway toward building inclusive VL datasets and equitable AI systems.

Culture Affordance Atlas: Reconciling Object Diversity Through Functional Mapping

Large Language Models are widely used to generate and adapt cultural texts, yet the depth of their cultural representation remains poorly quantified. Intuitively, as a narrative text expands in length, the diversity of cultural words should scale proportionately. To formally test this, we evaluate the FairyTaleQA dataset, adapted by three models and introduce our primary contribution: the Contextual Stereotype Amplification Index (CSAI), an evaluation framework combining LLM-as-a-judge extraction, embedding-based cliché anchoring, and Natural Language Inference (NLI) congruence validation. By mapping the frequency of extracted Culture Specific Items (CSIs) against narrative length using Heaps' Law ($V = k \cdot T^\beta$), we present empirical evidence of a systematic limitation in current systems: they struggle to scale cultural diversity even under explicit cultural prompting. Models rapidly hit a "Cultural Vocabulary Ceiling," constrained to a fixed set of hyper-stereotypical terms. Furthermore, we demonstrate that merely optimizing for higher CSI frequency as done in prior works rewards logically broken tokenism. Our CSAI formulation actively penalizes such gratuitous stereotyping, offering a more principled approach to measuring and evaluating cultural homogenization in generative AI systems.

The Mirage of Diversity: Unmasking the Cultural Vocabulary Ceiling in LLMs

Large Language Models are increasingly deployed as writing assistants for users in the Global South, yet rewriting prompts can suppress institutionalized postcolonial varieties. We quantify South Asian English (SAsE) dialect erasure in a state-of-the-art open-weight model using a 500-sentence diagnostic benchmark (320 lexical and 180 syntactic markers). On Llama 3.3 70B, standard grammar correction retains only 26.0\% of markers (lexical 31.2\%; syntactic 16.7\%), while formalization is more destructive (14.0\% overall retention). For lexical items, we observe Americanization in 56.2\% (correction) and 59.4\% (formalization) of cases, typically via Standard American paraphrases. A simple dialect-aware prompt raises retention to 92.0\% and reduces lexical Americanization to 6.2\%, although some function-word phenomena remain resistant. A stress test shows even stronger suppression (6.7\% retention). We position dialect erasure within representational-harm and cultural-competence frameworks, and provide a replicable protocol for auditing writing-assistance systems.

The American Palimpsest: Quantifying South Asian English Dialect Erasure in LLMs

As Video Large Language Models (VideoLLMs) are deployed globally, understanding of the relevant cultural norms becomes essential for safety and accuracy. To advance cultural norm awareness evaluation in video models, we collect a dataset of over 3,000 annotations of cultural norms in video clips from popular US and Chinese TV shows. Each clip is labeled with cultural norms, adherence or violation labels, and verbal and non-verbal evidence supporting the label by at least three trained monocultural annotators with significant lived experience in the target culture. We conduct an analysis of annotator disagreements and construct a consensus-based benchmark for cultural norm understanding in videos, VideoNorms. Hierarchical linear modeling analysis of $7$ open-weight VideoLLMs' performance revealed that: 1) models exhibit cultural bias in norm adherence prediction; 2) models have more difficulty in providing non-verbal evidence compared to verbal for norm adherence or violation. Ablation studies confirm video modality is indeed necessary for high task performance, and scaling model size does not yield classification performance improvements. Our findings and data will contribute to culturally grounded video model training and evaluation.

VideoNorms: Benchmarking Cultural Norm Understanding of Video Language Models

LLMs are deployed globally, yet produce responses biased towards cultures with abundant training data. Existing cultural localization approaches such as prompting or post-training alignment are black-box, hard to control, and do not reveal whether failures reflect missing knowledge or poor elicitation. In this paper, we address these gaps using mechanistic interpretability to uncover and manipulate cultural representations in LLMs. Leveraging sparse autoencoders (SAEs), we identify interpretable features that encode culturally salient information and aggregate them into $\textbf{Cu}$ltural $\textbf{E}$mbeddings ($\textit{CuE}$). We use $\textit{CuE}$ both to analyze implicit cultural biases under underspecified prompts and to construct white-box steering interventions. Across multiple models, we show that $\textit{CuE}$ -based steering increases cultural faithfulness and elicits significantly rarer, long-tail cultural concepts than prompting alone. Notably, $\textit{CuE}$ -based steering is complementary to black-box localization methods, offering gains when applied on top of prompt-augmented inputs. This also suggests that models do benefit from better elicitation strategies, and don't necessarily lack long-tail knowledge representation, though this varies across cultures. Our results provide both diagnostic insight into cultural representations in LLMs and a controllable method to steer towards desired cultures.

Steering LLMs for Culturally Localized Generation

Large language models (LLMs) are increasingly deployed in multilingual settings, yet little is known about whether their moral and social judgments remain consistent across languages. In particular, when faced with moral and social dilemmas, LLMs must often implicitly or explicitly assign responsibility — to an individual, to broader social forces, or across multiple parties — a process known as responsibility attribution. This study investigates whether responsibility attributions vary across languages, whether any observed variation persists across thematic domains, and whether the degree of variation differs across LLMs. We evaluate three models (GPT-5.2, Gemini-2.5-Pro, and LLaMA-3.3-70B) across 12 scenarios spanning six thematic domains (marriage, career, authority, gender, elder care, and family). Each model was prompted to attribute responsibility for each scenario by selecting from four options: the primary individual, a secondary interpersonal actor, a broader societal factor, or distributed responsibility shared across multiple parties. Results reveal a significant overall association between language and responsibility attribution (Cramér's $V = 0.24$) that persists within every thematic domain ($V = 0.26$--$0.53$). The magnitude of cross-language variation is strongly model-dependent: GPT-5.2 and Gemini-2.5-Pro show modest shifts ($V \approx 0.19$), while LLaMA-3.3-70B exhibits substantially stronger divergence ($V = 0.52$). These findings suggest that normative consistency across languages cannot be assumed and should be treated as a distinct dimension of model evaluation.

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Cultural Markers and Narrative Homogeneity in AI-Generated Stories

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Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph

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