Feature-Conditional vs. Class-Conditional Alignment in LLM Robustness on Imbalanced Reasoning Benchmarks

Natural language processing (NLP) has significantly transformed in the last decade, especially in the field of language modeling. Large language models (LLMs) have achieved SOTA performances on natural language understanding (NLU) and natural language generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in language models. In this paper, we examine the different aspects of language models, which started with a few million parameters but have reached the size of a trillion in a very

Research goal: How does feature-conditional alignment compared to class-conditional alignment affect the robustness of large language models on imbalanced reasoning benchmarks?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.0/10.