TabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages

We present TabuLM, the first language model pre-trained on Kinyarwanda tabular data. Kinyarwanda is a morphologically rich Bantu language spoken by over 12 million people in Rwanda, yet lacks any dedicated tabular representation learning resource. TabuLM extends KinyaBERT-large, a two-tier morphological transformer, with additive row, column, and cell-type embeddings and a learned table-structure attention bias that sharpens same-row and same-column attention. Pre-training uses two new objectives: Masked Cell Recovery (MCR), which masks entire cells and forces reconstruction from row and column context, and Column Type Prediction (CTP), which predicts column semantic types from observed cell values. We pre-train on 172 Rwandan government tables (~35,000 cells) from NISR, RAB, REB, and MoH open-data portals, and introduce TabQA-kin, the first native Kinyarwanda table question-answering benchmark comprising 526 QA pairs across 31 tables and four question types. TabuLM achieves 62.0% exact match on TabQA-kin, outperforming KinyaBERT-large by 5.7 EM points and all multilingual baselines (mBERT 49.3%, XLM-R 50.0%) by 11.7-12.7 points. Analysis shows that structural table embeddings are most decisive for comparison and lookup questions, while morphological awareness provides complementary gains. Our code, data, and pre-trained checkpoint are publicly available.

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fact retrieval from small tables and overlook the challenges of large multi-tabular datasets, external knowledge integration, and exploratory insight discovery. We introduce DataGovBench, a benchmark derived from governmental open data designed to evaluate LLMs in practical scenarios. The benchmark includes two tasks: Table QA that requires solving complex decomposable questions and producing textual answers or visualizations, and Table Insight that evaluates the ability of models to generate expert-level findings through exploratory data analysis. Comprehensive experiments with state-of-the-art LLMs, both with and without agentic frameworks, reveal significant performance gaps across both tasks. These results suggest that current LLM-based systems remain far from satisfying the demands of real-world data analytics. DataGovBench provides a challenging benchmark for advancing research on LLMs capable of both answering analytical queries and discovering insights from data. Code and sample data are available at https://github.com/SoHasegawa/datagovbench.

KrishokChat: A Provenance-Traceable Multi-Task Bengali Agricultural Benchmark with Safety-Critical Chemical Advisory

We introduce KrishokChat, an 85,979-instance Bengali agricultural benchmark built from 284 government publications, 13 institutions, and six regional dialects. The benchmark comprises four tracks: General Knowledge QA, Treatment QA, Safety Refusal and Re-query, and Table QA. It also includes a 1,000-query Real-World Farmer Benchmark collected independently from field interviews to measure transfer to authentic farmer language. Every extracted instance retains provenance at the citation level. Treatment QA also provides a structured chemical-trace array for dosage-level auditability. We evaluated five zero-shot baselines and one fine-tuned model. Closed-book knowledge proves insufficient regardless of model scale. Oracle evidence narrows the gap, but leaves a persistent floor of 4.05 to 7.00% of chemical hallucinations. Fine-tuning on KrishokChat substantially outperforms the strongest zero-shot baseline on General QA Token F1. However, structured table reasoning and farmer-language transfer remain largely unsolved. An exploratory analysis further reveals that fine-tuning reduces refusal behavior on safety-critical queries, indicating that additional safety alignment is required beyond supervised fine-tuning. We release KrishokChat as a traceable benchmark and audit resource to support grounded and safety-aware agricultural language modeling for Bengali.