Question Answering over large tables is challenging due to the difficulty of reasoning required in linking information from different parts of a table, such as heading and metadata to the values in the table and information needs. We investigate using Large Language Models (LLM) for tabular reasoning, where, given a pair of a table and a question from the DataBench benchmark, the models generate answers. We experiment with three techniques that enables symbolic reasoning through code execution: a direct code prompting (DCP) approach, ‘DCP_Py’, which uses Python, multi-step code (MSC) prompting ‘MSC_SQL+FS’ using SQL and ReAct prompting, ‘MSR_Py+FS’, which combines multi-step reasoning (MSR), few-shot (FS) learning and Python tools. We also conduct an analysis exploring the impact of answer types, data size, and multi-column dependencies on LLMs’ answer generation performance, including an assessment of the models’ limitations and the underlying challenges of tabular reasoning in LLMs.

Finding evidence for claims from content presented in experimental results of scientific articles is difficult. The evidence is often presented in the form of tables and figures, and correctly matching it to scientific claims presents automation challenges. The Context24 shared task is launched to support the development of systems able to verify claims by extracting supporting evidence from articles. We explore different facets of this shared task modelled as a search problem and as an information extraction task. We experiment with a range of methods in each of these categories for the two sub-tasks of evidence identification and grounding context identification in the Context24 shared task.

This paper introduces our system for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) competition. Our team focused on the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. To achieve our goal, we utilized transfer learning by fine-tuning pre-trained language models (PLMs) on the competition dataset. Our approach involved combining a BERT-based PLM with external knowledge to provide additional context to the model. In this report, we present our findings and results.

The published materials science literature contains abundant description information about synthesis procedures that can help discover new material areas, deepen the study of materials synthesis, and accelerate its automated planning. Nevertheless, this information is expressed in unstructured text, and manually processing and assimilating useful information is expensive and time-consuming for researchers. To address this challenge, we develop a Machine Learning-based procedural information extraction and knowledge management system (PIEKM) that extracts procedural information recipe steps, figures, and tables from materials science articles, and provides information retrieval capability and the statistics visualization functionality. Our system aims to help researchers to gain insights and quickly understand the connections among massive data. Moreover, we demonstrate that the machine learning-based system performs well in low-resource scenarios (i.e., limited annotated data) for domain adaption.

In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.