TabuLM is a new language model pre‑trained on Kinyarwanda tabular data, extending KinyaBERT‑large with row, column, and cell‑type embeddings and a table‑structure attention bias. It introduces two pre‑training objectives—Masked Cell Recovery and Column Type Prediction—and is trained on 172 Rwandan government tables. On the TabQA‑kin benchmark, TabuLM achieves 62.0% exact match, outperforming KinyaBERT‑large and multilingual baselines by significant margins.
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 dedi...
KinyaEmbed is the first sentence‑embedding model specifically designed for Kinyarwanda, built on KinyaBERT‑large and trained through a four‑stage curriculum that incorporates paraphrase pairs, translated MNLI triplets, OPUS‑100 translation pairs, and high‑quality KinyaCOMET pairs. It outperforms existing multilingual embeddings on the SemRel2024‑rw benchmark, achieving a Spearman ψ of 0.7298, and introduces the Wiki‑RW‑STS benchmark of 300 contamination‑free Kinyarwanda sentence pairs. All model checkpoints, filtered pairs, and the new benchmark are publicly released.
The paper introduces a reproducible benchmark for evaluating attention mechanisms in tabular foundation models, focusing on the distinct row and column attention patterns that differ from language model attention. It compares several backends—Torch SDPA, FlashAttention variants, vLLM, and SageAttention—across realistic tabular shapes on A100, H100, and B200 GPUs, revealing that optimal backend choice varies by attention type, hardware, and model specifics. The study finds FlashAttention generally performs best, but CuDNN can outperform it for column attention on longer sequences, while SageAttention excels for large row sequences beyond 16k rows.
Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rel...
arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
Causilo is a new tabular foundation model that delivers state‑of‑the‑art predictive performance while achieving exceptionally fast inference. On the TabArena benchmark it scores 1785.4 Elo with a median inference time of 0.10 seconds per 1 K test samples, outperforming TabPFN‑3.5‑Fast by 31.6% in speed and reaching the performance–efficiency Pareto frontier. The architecture builds on TabICL’s column‑then‑row design, adding a row‑refinement module that exchanges information among cell representations before a final column stage, and uses cross‑attention with a fixed number of summary tokens to keep attention cost linear in the number of features.
arXiv:2605. 20254v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning.
arXiv:2607.15209v4 Announce Type: replace Abstract: Multilingual pre-trained language models such as XLM-R perform well for major languages but struggle with low-resource Ge'ez-script languages, larg...
arXiv:2609.17458v1 Announce Type: cross Abstract: Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering...
arXiv:2605.16893v2 Announce Type: replace Abstract: Recent studies have introduced memory-augmented architectures that decouple knowledge storage from neural computation, enabling more direct access...
The paper investigates how Tabular Foundation Models (TFMs) can achieve strong transfer learning by self‑supervised pre‑training on a single real table. It finds that a table’s usefulness is largely determined by its number of features rather than instances, and that fine‑grained column‑level preprocessing improves downstream performance while dataset‑level filtering does not. The authors propose that tabular in‑context generalization is primarily retrieval‑based, with models learning to identify and aggregate relevant examples from the provided context.
Relational BabyLM is a decoder‑only Transformer that replaces standard self‑attention with a Dual Attention Transformer (DAT) to separate object‑level lexical features from structural/relational information. The model incorporates a Next‑Latent Prediction objective to compress history into a dense belief state and introduces a RoPE‑based symbol‑retrieval mechanism. On the BabyLM 2026 challenge, the best model ranks 6th overall and 3rd on the NLP‑task subset, outperforming GPT‑2 on most benchmarks and achieving the highest EWoK score among strict‑track entries.