Natural language understanding over tabular data has played a significant role in data discovery tasks such as joinable and unionable table search. State-of-the-art approaches adopt large language models (LLMs) pre-trained over massive text corpora to learn and evaluate the table semantic relatedness. Existing methods typically follow a pretrain-and-finetune paradigm, namely fine-tuning an LLM using tabular data with table relatedness labels. To enhance model’s understanding of tabular data, recent studies include auxiliary tasks such as entity resolution and column type classification in the fine-tuning phase. In spite of achieving performance gain from these supervisions, there is a lack of study on how these supervisions complement or even contrast each other, leading to a subpar performance on the final data discovery tasks. In this paper, we propose a simple yet effective multi-task fine-tuning framework named DiscoverGPT that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task. Moreover, DiscoverGPT is plug-and-play that allows a broad range of open-domain auxiliary tasks to be incorporated, by utilizing the generative power of LLMs. We demonstrate the usability and effectiveness of DiscoverGPT with baseline comparisons and ablation studies. DiscoverGPT outperforms the best performing baseline by up to 7% in F1 score.

Given a query table, how can we effectively discover multi-key joinable tables on the web? This can be seen as a retrieval task, where users can lookup on the web for tables related to an existing one. Searching and discovering such joinable tables is critical to data analysts and data scientists for reporting, establishing correlations and training machine learning models. Existing joinable table search methods have mostly focused on single key (unary) joins, where a single column is the join key. However, these methods are ineffective when dealing with join keys composed of multiple columns (n-ary joins), which are prevalent on web table corpora. In this paper, we introduce PolyJoin, which finds multi-key semantically-joinable tables on the web, given a query table. PolyJoin employs a multi-key encoder and a novel self-supervised training method to generate the representations of multiple join keys, preserving the alignment across multiple columns. In particular, PolyJoin is equipped with a hierarchical contrastive learning technique to further enhance the model’s semantic understanding of multi-key joinable tables. PolyJoin outperforms the state-of-the-art methods by 2.89% and 3.67% with respect to MAP@30 and R@30 on two real-world web table benchmarks, respectively.

In-context learning (ICL) adapts Large Language Models (LLMs) to new tasks, without requiring any parameter updates, but few annotated examples as input. In this work, we investigate selective annotation for ICL, where there is a limited budget for annotating examples, similar to low-budget active learning (AL). Although uncertainty-based selection is unreliable with few annotated data, we present CoverICL, an adaptive graph-based selection algorithm, that effectively incorporates uncertainty sampling into selective annotation for ICL. First, CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples. Then, CoverICL employs uncertainty estimation by the LLM to identify hard examples for the task. Selective annotation is performed over the active graph of the hard examples, adapting the process to the particular LLM used and the task tackled. CoverICL selects the most representative examples by solving a Maximum Coverage problem, approximating diversity-based sampling. Extensive experiments on ten datasets and seven LLMs show that, by incorporating uncertainty via coverage on the active graph, CoverICL (1) outperforms existing AL methods for ICL by 2–4.6% accuracy points, (2) is up to 2x more budget-efficient than SOTA methods for low-budget AL, and (3) generalizes better across tasks compared to non-graph alternatives.

Recent advances in large language models have revolutionized many sectors, including the database industry. One common challenge when dealing with large volumes of tabular data is the pervasive use of abbreviated column names, which can negatively impact performance on various data search, access, and understanding tasks. To address this issue, we introduce a new task, called NameGuess, to expand column names (used in database schema) as a natural language generation problem. We create a training dataset of 384K abbreviated-expanded column pairs using a new data fabrication method and a human-annotated evaluation benchmark that includes 9.2K examples from real-world tables. To tackle the complexities associated with polysemy and ambiguity in NameGuess, we enhance auto-regressive language models by conditioning on table content and column header names – yielding a fine-tuned model (with 2.7B parameters) that matches human performance. Furthermore, we conduct a comprehensive analysis (on multiple LLMs) to validate the effectiveness of table content in NameGuess and identify promising future opportunities. Code has been made available at https://github.com/amazon-science/nameguess.

The success of sites such as ACLED and Our World in Data have demonstrated the massive utility of extracting events in structured formats from large volumes of textual data in the formof news, social media, blogs and discussion forums. Event extraction can provide a window into ongoing geopolitical crises and yield actionable intelligence. In this work, we cast socio-political event extraction as a machine reading comprehension (MRC) task. % With the proliferation of large pretrained language models Machine Reading Comprehension (MRC) has emerged as a new paradigm for event extraction in recent times. In this approach, extraction of social-political actors and targets from a sentence is framed as an extractive question-answering problem conditioned on an event type. There are several advantages of using MRC for this task including the ability to leverage large pretrained multilingual language models and their ability to perform zero-shot extraction. Moreover, we find that the problem of long-range dependencies, i.e., large lexical distance between trigger and argument words and the difficulty of processing syntactically complex sentences plague MRC-based approaches. To address this, we present a general approach to improve the performance of MRC-based event extraction by performing unsupervised sentence simplification guided by the MRC model itself. We evaluate our approach on the ICEWS geopolitical event extraction dataset, with specific attention to ‘Actor’ and ‘Target’ argument roles. We show how such context simplification can improve the performance of MRC-based event extraction by more than 5% for actor extraction and more than 10% for target extraction.

Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU). Since annotated datasets are only available for a handful of languages, our work focuses particularly on a zero-shot scenario where the target language is unseen during training. In the context of zero-shot learning, this task is typically approached using representations from pre-trained multilingual language models such as mBERT or by fine-tuning on data automatically translated into the target language. We propose a novel method which augments monolingual source data using multilingual code-switching via random translations, to enhance generalizability of large multilingual language models when fine-tuning them for downstream tasks. Experiments on the MultiATIS++ benchmark show that our method leads to an average improvement of +4.2% in accuracy for the intent task and +1.8% in F1 for the slot-filling task over the state-of-the-art across 8 typologically diverse languages. We also study the impact of code-switching into different families of languages on downstream performance. Furthermore, we present an application of our method for crisis informatics using a new human-annotated tweet dataset of slot filling in English and Haitian Creole, collected during the Haiti earthquake.