Synthetic-to-Real Data Ratio Effects on Tabular Foundation Model F1 Score Variance in CausalMixFT

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and f

Research goal: How does the ratio of synthetic-to-real data in CausalMixFT affect the F1 score variance of tabular foundation models on TabFact across multiple random seeds?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.