Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from the given text, mainly including four elements, i.e., aspect category, sentiment polarity, aspect term, and opinion term. Extracting pair, triple, or quad of elements is defined as compound ABSA. Due to its challenges and practical applications, such a compound scenario has become an emerging topic. Recently, large language models (LLMs), e.g. ChatGPT and LLaMA, present impressive abilities in tackling various human instructions. In this work, we are particularly curious whether LLMs still possess superior performance in handling compound ABSA tasks. To assess the performance of LLMs, we design a novel framework, called ChatABSA. Concretely, we design two strategies: constrained prompts, to automatically organize the returned predictions; post-processing, to better evaluate the capability of LLMs in recognition of implicit information. The overall evaluation involves 5 compound ABSA tasks and 8 publicly available datasets. We compare LLMs with few-shot supervised baselines and fully supervised baselines, including corresponding state-of-the-art (SOTA) models on each task. Experimental results show that ChatABSA exhibits excellent aspect-based sentiment analysis capabilities and overwhelmingly beats few-shot supervised methods under the same few-shot settings. Surprisingly, it can even outperform fully supervised methods in some cases. However, in most cases, it underperforms fully supervised methods, and there is still a huge gap between its performance and the SOTA method. Moreover, we also conduct more analyses to gain a deeper understanding of its sentiment analysis capabilities.

Aspect sentiment quad prediction (ASQP) aims to predict four aspect-based elements, including aspect term, opinion term, aspect category, and sentiment polarity. In practice, unseen aspects, due to distinct data distribution, impose many challenges for a trained neural model. Motivated by this, this work formulates ASQP into the few-shot scenario, which aims for fast adaptation in real applications. Therefore, we first construct a few-shot ASQP dataset (FSQP) that contains richer categories and is more balanced for the few-shot study. Moreover, recent methods extract quads through a generation paradigm, which involves converting the input sentence into a templated target sequence. However, they primarily focus on the utilization of a single template or the consideration of different template orders, thereby overlooking the correlations among various templates. To tackle this issue, we further propose a Broad-view Soft Prompting (BvSP) method that aggregates multiple templates with a broader view by taking into account the correlation between the different templates. Specifically, BvSP uses the pre-trained language model to select the most relevant k templates with Jensen–Shannon divergence. BvSP further introduces soft prompts to guide the pre-trained language model using the selected templates. Then, we aggregate the results of multi-templates by voting mechanism. Empirical results demonstrate that BvSP significantly outperforms the state-of-the-art methods under four few-shot settings and other public datasets. Our code and dataset are available at https://github.com/byinhao/BvSP.