Singapore

Guardian models monitor and regulate the outputs of user-facing AI systems. However, current guardian models fall short in two key ways. First, they are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerable to evolving harms, cross-lingual safety failures, and cultural misalignment. Second, most guardian models rely on rigid, predefined safety categories that do not generalize across diverse linguistic and sociocultural contexts. Ensuring robust safety requires flexible, runtime-enforceable policies and benchmarks that reflect local norms, harm scenarios, and cultural expectations. We introduce UbuntuGuard, the first African policy-based safety benchmark built from adversarial queries authored by 155 domain experts across sensitive fields, including healthcare, education, government, and finance. From these expert-crafted queries, we derive context-specific safety policies and reference responses that capture culturally grounded risk signals, enabling policy-aligned evaluation of guardian models. We evaluate six state-of-the-art guardian models, including static, dynamic, and multilingual variants, under multiple scenarios. Our findings reveal that existing English-centric benchmarks overestimate real-world multilingual safety, cross-lingual transfer provides partial but insufficient coverage, and dynamic models, while better equipped to leverage policies at inference time, still struggle in fully localized African-language contexts. These findings highlight the urgent need for multilingual, culturally grounded safety benchmarks to enable the development of reliable and equitable guardian models for low-resource languages.


AAAI 2026

UbuntuGuard: A Policy-Based Safety Benchmark for Low-Resource African Languages

safety systems

multilingual systems

guardrails

benchmarks

safety

low resource languages

evaluation

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 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Static leaderboards and single turn judgments correlate weakly with deployment outcomes, especially in multilingual and resource constrained settings. This position paper argues that credible evaluation hinges on verifiability: ex ante specifications that permit observable checks, repeatable scoring, and auditable evidence. We propose a minimal standard that makes verifiability first class while remaining compatible with existing workflows. The standard has four artifacts: a task schema, a validator entry point, a run card, and required reporting fields. We ground the proposal in prior work on coverage and transparency and on specification based checks. We present a prototype evaluation task for schema constrained instruction following with robustness probes and a multilingual protocol, and we attach measurement and governance procedures that link scores to validity arguments. The goal is to replace generic win rates with verifiable claims about task success that better predict real use across languages and contexts.

Beyond Static Leaderboards: A Roadmap to Naturalistic, Functional Evaluation of LLMs

Underserved and extremely low-resource languages challenge current language technologies, especially when lexical borrowing and synonymy undermine exact-match assumptions. We study Bahnaric-Vietnamese lexical mapping as a step toward meaning-preserving sentence translation. Unlike prior work based on static embeddings and Mean Squared Error (MSE) alignment, we learn sentence-aware word representations with a small multilingual transformer pretrained on Vietnamese, adapt it with Low-rank adaptation (LoRA) for parameter efficiency, and align Bahnaric-Vietnamese pairs using a two-layer projection trained with InfoNCE contrastive loss. We exploit a new community-sourced lexicon of approximately 10,000 Bahnaric-Vietnamese pairs collected with local partners, capturing one-to-one, one-tomany, and many-to-one anchor relations as well as extensive lexical borrowing. Experiments evaluate retrieval-style alignment with Precision at K (P@K) and Mean Reciprocal Rank (MRR), as well as sentence translation using top-1 accuracy, Bilingual Evaluation Understudy (BLEU) and Character ngram F-score (ChrF). On the ∼1k lexicon, our best model attains P@1 ≈ 0.53 and MRR ≈ 0.62, substantially improving over a static-embedding MSE baseline, while on the richer ∼10k community lexicon it reaches comparable sentencelevel top-1 accuracy despite slightly lower BLEU and chrF, highlighting both the benefits of the expanded resource and the remaining challenges of synonym-rich, low-frequency vocabulary.

Sentence-Aware Bahnaric-Vietnamese Lexical Mapping with Contrastive Contextual Representations

We present ENLIVEN-1000, a unified framework for endangered and low-resource language revitalization that integrates broad-coverage language identification (LID), machine translation (MT), and LLM-generated synthetic data—aimed at expanding safe, equitable NLP support for communities historically excluded from mainstream tools. We compile a text corpus for 1154 languages (1069 endangered or low-resource) from public sources and train a fastText-based LID model covering this vast set. The LID system achieves high detection quality with F1 ≈ 0.99 and FPR ≈ 3×10−6, substantially broadening reliable coverage beyond existing solutions. Focusing on five diverse endangered languages—Carpathian Romani, Chuj, Sunwar, Kapingamarangi, and Inuktitut—we fine-tune a 600M-parameter NLLB-200 model for translation. Our fine-tuned models outperform zero-shot baselines and even proxy models trained on related, high-resource languages, in both directions (endangered -> English and English -> endangered). We further use GPT-4o to generate synthetic parallel data, demonstrating that augmenting limited real data with LLM-generated text yields substantial MT improvements. These results illustrate a practical path toward scaling NLP support to hundreds of under-resourced languages. We discuss implications for language revitalization and ethical considerations in working with endangered language communities.



ENLIVEN-1000: A Comprehensive Revitalization Framework for 1000+ Endangered Languages via Broad-Coverage LID and LLM-Augmented MT

Large language models (LLMs) enable scalable conversational support for postpartum depression (PPD), yet current systems insufficiently account for intra-lingual cultural variation even within high-resource languages such as Chinese. Dialectal phrasing, local idioms, and culturally embedded expressions (e.g., Northeastern Mandarin "zhabayue de teng" (humorous discomfort) or the Southern Min "xin-gua-a-tia
" (deep sorrow)) often produce misinterpretation, safety-critical ambiguity, or emotionally inappropriate responses in PPD-related dialogues. We introduce CAMA (Culturally Adaptive Multi-Agent Co-Design Framework), a lightweight cultural-sensitivity detection and alignment framework that identifies dialect-specific linguistic cues and supplements LLMs with contextual socio-cultural grounding without performing clinical diagnosis. Our approach integrates culturally aware prompting and intervention logic to enhance empathy, safety, relevance, and user trust. This work highlights that cultural fairness in mental-health LLMs must consider intra-language diversity, not only cross-lingual disparity. CAMA provides a practical pathway towards culturally aligned, safe, and trustworthy mental-health dialogue systems.

CAMA: A Culturally Adaptive Multi-Agent Framework for Postpartum Depression Support in Multilingual and Low-Resource Settings

Tokenization serves as a crucial preprocessing step in multilingual language models, affecting performance in both high-resource and low-resource languages. However, current tokenizers seem to adopt language biases due to unbalanced training datasets, leading to a poorly optimized tokenizer for underrepresented languages. This research examines the impact of balanced multilingual datasets on the performance of tokenizers trained with the Byte Pair Encoding, WordPiece, and Unigram Language Model algorithms. We build balanced corpora from various sources to study the impact of vocabulary size on 15k, 30k, 50k dataset scales. The trained tokenizers are assessed through intrinsic metrics, including Subword Fertility and Normalized Sequence Length, as well as through extrinsic performance on downstream tasks like Part-of-Speech tagging, Named Entity Recognition, and Machine Translation. We build custom data sets along with customized evaluation pipelines to enable consistent comparisons across nine languages using models built into standard NLP frameworks. Our observations reinforce the importance of a balanced dataset when training tokenizers and, in turn, advance the development of equitable and robust multilingual NLP systems.

From Bias to Balance: How Multilingual Dataset Composition Affects Tokenizer Performance Across Languages

Neural Machine Translation (NMT) for low-resource and underserved languages remains challenging due to the severe lack of parallel corpora, linguistic tools, and evaluation resources. The issue is evident in Vietnam, where the ethnolinguistic minority languages Tày (Tai–Kadai) and Bahnar (Austroasiatic) hold cultural significance but remain digitally under-represented. Data Augmentation (DA) offers a cost-effective remedy; however, most existing techniques were designed for high-resource analytic languages and are often applied heuristically without assessing their linguistic compatibility. In this work, we present the first systematic study of DA for two minority language pairs, Tày–Vietnamese and Bahnar–Vietnamese, within a three-stage language model pipeline consisting of Vietnamese-based initialization, monolingual adaptation, and supervised fine-tuning. We train two independent encoder–decoder NMT systems to isolate augmentation effects and analyze how linguistic typology shapes augmentation behavior. Our experiments show that meaning-preserving DA methods consistently improve translation adequacy and linguistic faithfulness, whereas several widely used techniques introduce semantic or structural degradation. Through quantitative evaluation and typology-aware linguistic analysis, we derive practical guidelines for selecting DA strategies in extremely low-resource and typologically diverse settings. We additionally release newly digitized high-quality bilingual corpora and trained models to facilitate future research and community-centered NLP development.

Not All Data Augmentation Works: A Typology-Aware Study for Low-Resource Neural Machine Translation in Vietnamese Ethnic Minority Languages

Evaluating the safety and alignment of AI systems remains a critical challenge as foundation models grow increasingly sophisticated. Traditional evaluation methods rely heavily on human expert review, creating bottlenecks that cannot scale with the rapid pace of AI development. We introduce Jo.E (Joint Evaluation), a novel multi-agent collaborative framework that combines large language model evaluators, specialized AI agents, and strategic human expert involvement to conduct comprehensive safety assessments. Our framework employs a five-phase evaluation pipeline that systematically identifies vulnerabilities across multiple safety dimensions including adversarial robustness, fairness, ethics, and accuracy. Through extensive experiments on state-of-the-art models including GPT-4o, GPT-5, Llama 3.2, Phi 3, and Claude Sonnet 4, we demonstrate that Jo.E achieves approximately 22% improvement in vulnerability detection while reducing human expert time requirements by 54% compared to traditional evaluation approaches. Our results show that automated collaborative evaluation can significantly enhance both the efficiency and effectiveness of AI safety assessment without sacrificing rigor or comprehensive coverage.

Jo.E(Joint Evaluation) : A Multi-Agent Collaborative Framework for Comprehensive AI Safety Evaluation of Language Models

Information retrieval in Large Language Models (LLMs) is increasingly recognized as intertwined with generation capabilities rather than mere lookup. While longer contexts are often assumed to improve retrieval, the effects of intra-context interference remain understudied. To address this, we adapt the proactive interference (PI) paradigm from cognitive science, where earlier information disrupts recall of newer updates. In humans, susceptibility to such interference is inversely linked to working memory capacity. We introduce PI-LLM, an evaluation that sequentially streams co-referenced key–value updates and queries only the final values. Although these final values are clearly positioned just before the query, LLM retrieval accuracy declines log-linearly toward zero as co-referenced interference accumulates; errors arise from retrieving previously overwritten values. Attempts to mitigate interference via prompt engineering (e.g., instructing models to ignore earlier input) yield limited success. These findings reveal a fundamental constraint on LLMs’ ability to disentangle interference and flexibly manipulate information, suggesting a working memory bottleneck beyond mere context access. For Agentic systems, reliable operation hinges on reconciling past and present states. Proactive interference corrupts long-horizon state maintenance; dependable agent tracking therefore needs explicit memory control and interference-aware context management. We expose a “know, cannot do” failure. Coreference-only needles expose a plan–execute disjunction: LLMs form the correct last-value retrieval plan but fail to carry it out, with execution reliability declining systematically with task complexity. Code and data will be publicly available.

Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Length

While a multi-agent approach based on large language models (LLMs) represents a promising strategy to surpass the capabilities of single models, its success is critically dependent on synergistic team composition. However, forming optimal teams is a significant challenge, as the inherent opacity of most models obscures the internal characteristics necessary for effective collaboration. In this paper, we propose an interaction-centric framework for automatic team composition that does not require any prior knowledge including their internal architectures, training data, or task performances. Our method constructs a "language model graph" that maps relationships between models from the semantic coherence of pairwise conversations, and then applies community detection to identify synergistic model clusters. Our experiments with diverse LLMs demonstrate that the proposed method discovers functionally coherent groups that reflect their latent specializations. Priming conversations with specific topics identified synergistic teams which outperform random baselines on downstream benchmarks and achieve comparable accuracy to that of manually-curated teams based on known model specializations. Our findings provide a new basis for the automated design of collaborative multi-agent LLM teams.

The Geometry of Dialogue: Graphing Language Models to Reveal Synergistic Teams for Multi-Agent Collaboration

Large language models (LLMs) often generate fluent
but factually incorrect statements despite having access
to relevant evidence, a failure mode rooted in how they
allocate attention between contextual and parametric
knowledge. Understanding and steering this internal
behavior is key both for trustworthy deployment and
for scientific interpretability of model mechanisms.
We introduce COMPASS (Context-Modulated PID At-
tention Steering System), a lightweight, interpretable
control framework that embeds a model-based feedback
loop directly within decoding. COMPASS quantifies
context reliance via a transparent metric, the Context
Reliance Score (CRS), which serves as an online probe
of how attention heads ground generation in evidence.
Using this interpretable signal, a PID controller dynam-
ically modulates attention heads to maintain factual
consistency without retraining or multi-pass decoding.
Across benchmarks (HotpotQA, XSum, HaluEval,
RAGTruth), COMPASS consistently reduces contextual
hallucination rates (2.8–5.8% absolute) while revealing
how distinct attention heads contribute to evidence
alignment. These results highlight feedback-driven in-
terpretability as a pathway toward scientific understand-
ing of LLM behavior.

Next from AAAI 2026

Beyond Static Leaderboards: A Roadmap to Naturalistic, Functional Evaluation of LLMs

Similar lecture

UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages.

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