Exploring the Dilemma of Causal Incoherence: A Study on the Approaches and Limitations of Large Language Models in Natural Language Inference

Jon F. Apaolaza, Begoña Altuna, Aitor Soroa, Inigo Lopez-Gazpio

Resumen


This research addresses the critical yet underappreciated problem in state-of-the-art Large Language Models (LLMs) known as the Reversal Curse (RC). The RC denotes a failure to infer bidirectional relationships that undermines logical reasoning capabilities. Under the RC, LLMs are unable to infer bidirectional relationships effectively leading to logical errors in deductive reasoning. If a model is trained on a sentence of the form “A relates to B”, it does not automatically generalize to the reverse form, “B relates to A”. Through a systematic literature review and experimental analysis, we highlight the difficulties in maintaining causal coherence in state-of-the-art LLMs. Recognizing the RC as a persistent problem across architectures, we review mitigation strategies including data augmentation and innovative training objectives to offer valuable insights into the root causes and discuss their limitations. This work aims to contribute to the development of more reliable and coherent AI systems.

Texto completo:

PDF