Analyzing the Role of Semantic Representations in the Era of Large Language Models (2024)

Abstract: Traditionally, natural language processing (NLP) models often use a rich set of features created by linguistic expertise, such as semantic representations. However, in the era of large language models (LLMs), more and more tasks are turned into generic, end-to-end sequence generation problems. In this paper, we investigate the question: what is the role of semantic representations in the era of LLMs? Specifically, we investigate the effect of Abstract Meaning Representation (AMR) across five diverse NLP tasks. We propose an AMR-driven chain-of-thought prompting method, which we call AMRCOT, and find that it generally hurts performance more than it helps. To investigate what AMR may have to offer on these tasks, we conduct a series of analysis experiments. We find that it is difficult to predict which input examples AMR may help or hurt on, but errors tend to arise with multi-word expressions, named entities, and in the final inference step where the LLM must connect its reasoning over the AMR to its prediction. We recommend focusing on these areas for future work in semantic representations for LLMs. Our code: https://github.com/causalNLP/amr_llm

## introduction formal representations of linguistic structure and meaning have long held an important role in the construction and evaluation of nlp systems. semantic representations such as abstract meaning representation (amr; @xcite are designed to distill the semantic information of text to a graph consisting of the entities, events, and states mentioned in a text and the relations between them. existing studies have shown the benefits of representations like amr in a variety of nlp tasks, such as paraphrase detection @xcite , machine translation @xcite , event extraction @xcite , figure 1 : the role of representation power in different fields. analogous to arabic numbers for math, amr is designed to efficiently and explicitly represent the semantic features of text. existing work using amr is concerned with trainable models, whereas we investigate the use of amr in the modern practical setup of pre-trained llms. however, learning to produce and operate over representations like amr is nontrivial, especially since amr data is limited. in contrast, modern nlp systems based on large language models (llms) learn to directly manipulate text very effectively @xcite , not only achieving high performance on a variety of tasks without using intermediate formal representations @xcite , but also achieving gains by directly using informal textual intermediates in methods such as chain-of-thought (cot) prompting @xcite . due to economic concerns @xcite @xcite , there is a growing trend to utilize readily available pre-trained llms in various application scenarios, without allocating additional resources for training or fine-tuning the models. these trends raise the question: what is the role of semantic representations in the era of llms, when no training or finetuning is involved? motivated by this question, we propose a theoretical formulation of representation power, and what it means to have an ideal representation for text, using ideas from @xcite . our key observation is that making use of even a very strong intermediate representation requires optimizing the model with regard to that representation; however, when using (out of the box) pretrained llms, the optimal representation will be the one which the llm can most effectively use on the basis of its pretraining, which might shift away from the optimal representation for a learnable mapping to the output space. in short, the a priori ideal representation for a task is not necessarily the ideal representation for an llm to use. given this, we empirically study how good amr is as an intermediate representation for llms. specifically, we answer the following three questions: (1) does amr help llm performance? (2) when does amr help, and when doesn't it? (3) why does it help or not help in these cases? on a diverse set of five nlp tasks, our experiments show that the contribution of amr in the era of llms is not as great as that in the traditional setup where we can optimize the model for the representation. amr causes a slight fluctuation of performance by -3 to +1 percentage points. however, we find that amr is helpful for a subset of samples. we also find that the next step for using amr to improve performance is likely not improving amr parser performance, but improving the llm's ability to map amr representations into the output space. in summary, the contributions of our work are as follows: ## formalizing representation power in this section, we propose a framework to formulate representation power in both the pre-llm era, where we do not outsource the training of the models, and the llm era, where a lot of practical settings are to optimize the representation with regard to given fixed llms. ## designing the amrcot experiments we introduce an amr-driven prompting method which we call amrcot, and investigate its performance on five datasets with five llms. ## q3: why does amr help/hurt? to understand why amr helps or hurts when it does, we look into the following subquestions: (1) how does parser-generated amr work compared with gold amr (section 6.1)? ( 2 ) what is the representation power of amr versus text when the other is ablated (section 6.2)? and (3) how does amr help in each step of the reasoning process (section 6.3)? ## conclusion in this work, we analyze the role of semantic representations in the era of llms. in response to the ongoing paradigm shift in the nlp community, we show that amr in general is not yet a representation immediately fit for pre-trained llms. however, our study shows that amr still helps on some samples. we also suggest that a potential direction to enhance amr's contribution to llms is to improve the understanding of llms over the schemes and symbols of amr, and map it to the reasoning of the respective nlp task. this work presents an effort to bridge the traditionally rich linguistic structures with the strength of llms.