Enhancing Language Models with Idiomatic Reasoning

摘要

Advancements in Large Language Models (LLMs) have significantly propelled the field of Natural Language Processing (NLP); however, nuanced reasoning in the presence of non-canonical language forms, such as figurative language, remains an intricate challenge. These language forms, integral to human communication, elude standard LLM comprehension due to their inherent non-compositionality, contextual ambiguity, and sparse representation in text corpora. Addressing these challenges, this paper introduces an innovative approach to seamlessly incorporate idiomatic knowledge into pre-trained language models (PTLMs). Our methodology first employs a multi-view data augmentation strategy that uses idiomatic instances representing one property to generate training data for various idiom-related tasks. When combined with a novel parameter-efficient tuning mechanism that accounts for the unique attributes of idiomatic language, we embed task-specific and idiomaticity-aware inductive biases within a PTLM. Integrating a meta-pretraining protocol based on meta-learning principles, further equips the model with enhanced adaptability to diverse downstream idiom-aware tasks. Empirical validation on diverse benchmarks centered around idiom comprehension and reasoning, demonstrates the efficacy of our approach. Notably, our model surpasses various parameter-efficient fine-tuning baselines outperforming the conventional full fine-tuning paradigms, thereby creating more contextually aware and linguistically robust language models.