In the fast-evolving world of Natural Language Processing (NLP), Large Language Models (LLMs) have become increasingly dominant. These models, trained on massive text datasets, can perform various tasks from translation to summarization without explicit linguistic features. This raises a crucial question: do LLMs still need traditional semantic representations, or are they a relic of the past? This question is explored in a new research paper, "Analyzing the Role of Semantic Representations in the Era of Large Language Models." The researchers investigated the impact of Abstract Meaning Representation (AMR), a graph-based semantic formalism, on five diverse NLP tasks using several LLMs, including GPT-4. The results reveal a nuanced picture. While AMR didn't consistently improve overall performance, it did show benefits in specific cases. The researchers introduced AMRCOT, an AMR-driven chain-of-thought prompting method. However, they found that it often hindered performance more than it helped. This suggests that while LLMs can leverage semantic information, directly providing AMR might not be the most effective approach. A deeper analysis revealed that AMR's impact varied depending on the linguistic features of the input text. For example, AMR was more helpful for sentences with complex words and structures, while it was less effective for sentences containing named entities or multi-word expressions. This suggests that AMR's strengths lie in disentangling complex semantic relationships, but it struggles with the nuances of lexical semantics. The research also highlights the importance of the LLM's ability to interpret and utilize the provided AMR. Even when GPT-4 could correctly identify the commonalities and differences between AMRs, it sometimes failed to integrate this information into the final decision-making process. This suggests that future research should focus on improving how LLMs interface with semantic representations. In conclusion, the role of semantic representations in the LLM era is complex. While the initial findings suggest that simply adding AMR to the input might not be a game-changer, the research opens up exciting avenues for future exploration. Improving LLMs' understanding of semantic formalisms and developing more effective ways to integrate them into the reasoning process could unlock the full potential of both LLMs and semantic representations.