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 provide expressive and structured semantic graphs that act as intermediates that simplify downstream NLP tasks. However%2C the instruction-following capability of large language models (LLMs) offers a shortcut to effectively solve NLP tasks%2C questioning the utility of semantic graphs. Meanwhile%2C recent work has also shown the difficulty of using meaning representations merely as a helpful auxiliary for LLMs. We revisit the position of semantic graphs in syntactic simplification%2C the task of simplifying sentence structures while preserving their meaning%2C which requires semantic understanding%2C and evaluate it on a new complex and natural dataset. The AMR-based method that we propose%2C AMRS%24^3%24%2C demonstrates that state-of-the-art meaning representations can lead to easy-to-implement simplification methods with competitive performance and unique advantages in cost%2C interpretability%2C and generalization. With AMRS%24^3%24 as an anchor%2C we discover that syntactic simplification is a task where semantic graphs are helpful in LLM prompting. We propose AMRCoC prompting that guides LLMs to emulate graph algorithms for explicit symbolic reasoning on AMR graphs%2C and show its potential for improving LLM on semantic-centered tasks like syntactic simplification.?quality=80&w=800)
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