Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to process the table. Inspired by prior work, we generate a collection of self-supervised table structure understanding tasks (e.g. navigate to a cell and row; transpose the table) and evaluate the performance differences when using eight formats. In contrast to past work, we introduce eight noise operations inspired by real-world messy data and adversarial inputs, and show that these can impact LLM performance across formats for different structural understanding tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"
Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to process the table. Inspired by prior work, we generate a collection of self-supervised table structure understanding tasks (e.g. navigate to a cell and row; transpose the table) […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Ananya Singha","user_id":"43440"},{"type":"user_nicename","value":"Jos\u00e9 Cambronero","user_id":"40531"},{"type":"user_nicename","value":"Sumit Gulwani","user_id":"33755"},{"type":"user_nicename","value":"Vu Le","user_id":"39174"},{"type":"user_nicename","value":"Chris 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