Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applications

arXiv:2405.01585v1 Announce Type: cross
Abstract: In recent times Large Language Models have exhibited tremendous capabilities, especially in the areas of mathematics, code generation and general-purpose reasoning. However for specialized domains especially in applications that require parsing and analyzing large chunks of numeric or tabular data even state-of-the-art (SOTA) models struggle. In this paper, we introduce a new approach to solving domain-specific tabular data analysis tasks by presenting a unique RAG workflow that mitigates the scalability issues of existing tabular LLM …

abstract applications art arxiv capabilities code code generation cs.ai cs.cl cs.ir data domains embedding embedding models finetuning general however language language models large language large language models mathematics parsing rag reasoning sota state tabular tabular data type