Language of study is an aspect of computational linguistics papers that is useful for analyses of trends and diversity in computational linguistics. This study introduces the first benchmark and evaluation of automated language of study extraction from computational linguistics publications. The benchmark containing 431 publications from the ACL Anthology, with 62 languages analyzed, was annotated. SciBERT and four large language models (LLMs), GPT-4o mini, Gemini 2.5 Flash, Claude 3.5 Haiku, and DeepSeek 3.2, were evaluated on the benchmark using different parts of the ACL Anthology papers. GPT-4o mini achieved the best exact match and Jaccard agreement scores of 0.646 and 0.687, respectively, which is slightly less than the agreement in human annotation. Gemini 2.5 Flash achieved the best micro F1 of 0.633. Models using the abstract for extraction were competitive with models using the full text, showing that accuracy can be achieved in language of study extraction without high computational costs. These findings demonstrate that LLMs are able to accurately identify the languages of study in computational linguistics papers, potentially reducing the time and cost of analyses in computational linguistics.

Resolving contradictions in biomedical literature requires more than factual recall; it demands identifying the hidden variables that explain divergent findings. Existing NLI benchmarks such as MedNLI operate at the sentence level and fail to capture document-level conflicts driven by differences in dosage, cell type, or study design. We introduce BioConflict, a benchmark of 250 expert-annotated paper pairs (500 abstracts) across ten biomedical topics, formalizing three tasks: conflict detection, contextual variable extraction, and consensus synthesis. We evaluate five general-purpose large language models and two domain-specific baselines, finding that general-purpose large language models achieve strong conflict detection (F1 up to 0.89) but exhibit brittle reasoning in synthesis, while domain-specific models lag significantly on all generative tasks. These findings highlight the need for context-aware biomedical AI capable of resolving, not merely retrieving, conflicting scientific evidence.

Large language models (LLMs) struggle with low-resource polysynthetic languages, yet the nature of their failures remains underexplored. We evaluate GPT-4o-mini, Gemma~3~27B, Llama~3.3~70B, and NLLB-200 on Spanish$\leftrightarrow$Wixárika translation using zero-shot and 5-shot prompting. All systems are unusable, scoring below 3 BLEU and 21 chrF. Qualitative analysis reveals that LLMs largely ignore source content and instead generate fluent hallucinations. Spanish outputs frequently include indigenous cultural stereotypes such as deer, deities, rain dance, and shamans, regardless of the input, while Wixárika outputs are repetitive across different inputs and morphologically implausible. Few-shot prompting yields model-dependent improvements, with Gemma and Llama improving substantially at higher shot counts while GPT-4o-mini remains flat. These results demonstrate that current LLMs are unable to represent polysynthetic morphology and instead default to exoticizing Indigenous culture and identity. We call for the development of inclusive morphological-aware modeling strategies and increased resource creation to ensure that Indigenous languages of the Americas are represented safely and accurately.

Kazakh is a Turkic language using the Arabic, Cyrillic, and Latin scripts, making it unique in terms of optical character recognition (OCR). Work on OCR for low-resource Kazakh scripts is very scarce, and no OCR benchmarks or images exist for the Arabic and Latin scripts. We construct a synthetic OCR dataset of 7,219 images for all three scripts with font, color, and noise variations to imitate real OCR tasks. We evaluated three multimodal large language models (MLLMs) on a subset of the benchmark for OCR and language identification: Gemma-3-12B-it, Qwen2.5-VL-7B-Instruct, and Llama-3.2-11B-Vision-Instruct. All models are unsuccessful with Latin and Arabic script OCR, and fail to recognize the Arabic script as Kazakh text, misclassifying it as Arabic, Farsi, and Kurdish. We further compare MLLMs with a classical OCR baseline and find that while traditional OCR has lower character error rates, MLLMs fail to match this performance. These findings show significant gaps in current MLLM capabilities to process low-resource Abjad-based scripts and demonstrate the need for inclusive models and benchmarks supporting low-resource scripts and languages.