{"id":1101414,"date":"2024-11-06T04:52:02","date_gmt":"2024-11-06T12:52:02","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1101414"},"modified":"2024-11-06T04:52:05","modified_gmt":"2024-11-06T12:52:05","slug":"tap4llm-table-provider-on-sampling-augmenting-and-packing-semi-structured-data-for-large-language-model-reasoning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/tap4llm-table-provider-on-sampling-augmenting-and-packing-semi-structured-data-for-large-language-model-reasoning\/","title":{"rendered":"TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning"},"content":{"rendered":"

Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data based on natural language (NL) questions. Existing solutions mainly tested on smaller tables face scalability issues and struggle with complex queries due to incomplete or dispersed data across different table sections. To alleviate these challenges, we propose TAP4LLM<\/strong> as a versatile pre-processor suite for leveraging LLMs in table-based tasks effectively. It covers several distinct components: (1) table sampling<\/em> to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation<\/em> to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization<\/em> to convert tables into various formats suitable for LLMs’ understanding. In each module, we design and compare several common methods under various usage scenarios, aiming to shed light on the best practices for leveraging LLMs for table-reasoning tasks. Our experiments show that our method improves LLMs’ reasoning capabilities in various tabular tasks and enhances the interaction between LLMs and tabular data by employing effective pre-processing.<\/p>\n","protected":false},"excerpt":{"rendered":"

Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data based on natural language (NL) questions. Existing solutions mainly tested on smaller tables face scalability issues and struggle with complex queries due to incomplete or dispersed data across different table sections. 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