The recent advancements in multimodal large language models (MLLMs) and large language models (LLMs) have significantly enhanced the capabilities of sentiment analysis and specialized domain applications. MLLMs are now being benchmarked for their ability to handle complex, multi-object sentiment analysis tasks, which reflects real-world complexities and requires models to independently assess the sentiment of each object. This development highlights the need for MLLMs to improve accuracy in such tasks, particularly as spatial distances between objects increase. Additionally, there is a growing focus on creating specialized benchmarks for MLLMs in specific domains, such as agriculture, where AgriBench and MM-LUCAS are pioneering efforts to evaluate and advance these models in agricultural contexts. These benchmarks not only test the models' understanding of complex images but also their ability to integrate detailed annotations and hierarchical data. In the realm of aspect-based sentiment analysis (ABSA), there is a shift towards more efficient and unified approaches, such as the transition-based pipeline and prompt-based generative sequence optimization networks, which aim to mitigate token-level bias and capture position-aware relations. These methods demonstrate superior performance over traditional pipelined frameworks, reducing error propagation and optimizing time complexity. Furthermore, LLMs are being comprehensively evaluated for their capabilities in ABSA, with innovative techniques like in-context learning and parameter-efficient fine-tuning showing promising results. These evaluations underscore the potential of LLMs to outperform smaller models in both fine-tuning-dependent and fine-tuning-free paradigms. Lastly, there is a notable trend towards developing natural language interfaces for specialized data exploration, exemplified by FathomGPT, which facilitates interactive investigation of ocean science data, enhancing the user experience through free-form exploration and optimized response times.

Noteworthy papers include 'MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image,' which introduces a novel dataset for multi-object sentiment analysis, and 'AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models,' which pioneers the evaluation of MLLMs in agriculture. 'PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis' presents a unified approach to optimizing generative models for ABSA, while 'FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data' showcases an innovative application of LLMs in ocean science data exploration.