In data science, predictive tasks such as classification, regression, and missing value imputation are fundamental challenges in tabular data analysis. This research investigates the application of Large Language Models (LLMs) to these tasks. While LLMs excel in natural language understanding, their effectiveness on structured tabular data remains limited due to minimal exposure during pretraining. To address this gap, we construct a large-scale corpus of annotated tables and introduce a tailored pretraining framework. Our trained model achieves significant improvements over baselines, with an average gain of 8.9% in classification and 10.7% in regression tasks. We further evaluate its performance in zero-shot and few-shot prediction, as well as in-context learning scenarios. Extensive experiments demonstrate substantial gains over existing benchmarks, highlighting the potential of LLMs for tabular data processing. Additionally, we apply our approach across multiple open-source LLMs and demonstrate its generalizability. This work establishes a new benchmark for enhancing tabular intelligence through LLM-based pretraining.

Large vision-language models (LVLMs) excel across diverse tasks involving concrete images from natural scenes.However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training datasets in scientific domains.To fill this gap, we introduce Multimodal ArXiv, consisting of ArXivCap and ArXivQA, for enhancing LVLMs scientific comprehension.ArXivCap is a figure-caption dataset comprising 6.4M images and 3.9M captions, sourced from 572K ArXiv papers spanning various scientific domains.Drawing from ArXivCap, we introduce ArXivQA, a questionanswering dataset generated by prompting GPT-4V based on scientific figures.ArXivQA greatly enhances open-sourced LVLMs' mathematical reasoning capabilities, achieving a 10.4% absolute accuracy gain on a multimodal mathematical reasoning benchmark.Furthermore, employing ArXivCap, we devise four vision-to-text tasks for benchmarking LVLMs.Evaluation results with state-of-the-art LVLMs underscore their struggle with the nuanced semantics of academic figures, while domainspecific training yields substantial performance gains.Our error analysis uncovers misinterpretations of visual context, recognition errors, and the production of overly simplified captions by current LVLMs, shedding light on future improvements.

Recent advancements in Natural Language Processing (NLP) have witnessed the groundbreaking impact of pretrained models, yielding impressive outcomes across various tasks. This study seeks to extend the power of pretraining methodologies to facilitating the prediction over tables in data science, a domain traditionally overlooked, yet inherently challenging due to the plethora of table schemas intrinsic to different tasks. The primary research questions underpinning this work revolve around the establishment of a universal pretraining protocol for tables with varied structures, the generalizability and transferability of learned knowledge across tasks, the adaptation to diverse downstream applications, and the incorporation of incremental columns over time. In response to these challenges, we introduce UniTabE, a straightforward yet effective method designed to process tables in a uniform manner, devoid of constraints imposed by specific table structures. UniTabE's core concept relies on representing each basic table element with a module, termed TabUnit. This is subsequently followed by a Transformer encoder to refine the representation. Moreover, our model is designed to facilitate pretraining and finetuning through the utilization of free-form prompts. In order to implement the pretraining phase, we curated an expansive tabular dataset comprising approximately 13 billion samples, meticulously gathered from the Kaggle platform. This research primarily centers on classification and regression tasks involving tabular data, and conducts rigorous experimental testing and analyses to validate the effectiveness of our methodology. The experimental results demonstrate UniTabE's superior performance against several baseline models across a multitude of benchmark datasets. This, therefore, underscores UniTabE's potential to significantly enhance the semantic representation of tabular data, thereby marking a significant stride for tabular data analysis.