arXiv:2501.14717v2 Announce Type: replace Abstract: Table modeling has progressed for decades. In this work, we revisit this trajectory and highlight emerging challenges in the LLM era, particularly the paradox of choice: the difficulty of attributing performance gains amid diverse base models and training sets in the context of table instruction tuning. We replicate four table LLMs by instruction-tuning three foundation models on four existing datasets, yielding 12 models. We then evaluate these models across 16 table benchmarks. Our study is the first to quantitatively disentangle the effect
The proliferation of various Large Language Models (LLMs) and training datasets necessitates a systematic evaluation to understand their true impact and address the 'paradox of choice'.
This meta-evaluation provides crucial insights into the efficacy of different foundational models and instruction tuning datasets for table-based tasks, guiding future AI development and application.
The study quantitatively disentangles the effects of base models and data, allowing for more informed decisions in developing and deploying Table LLMs, potentially streamlining research and development.
- · AI researchers focusing on structured data
- · Developers of data-centric AI systems
- · Companies investing in efficient LLM training
- · Developers using suboptimal LLM and data combinations
- · Researchers without systematic evaluation frameworks
Improved understanding of performance drivers for Table LLMs leads to more efficient model development.
Optimized Table LLMs enhance capabilities for data extraction, analysis, and generation across various industries.
Increased reliability and performance of AI in handling structured data could accelerate automation in fields like finance and scientific research.
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