Summary of Exploring Large Language Models For Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions, by Shezheng Song
Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions
by Shezheng Song
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the suitability of large language models (LLMs) for Multimodal Aspect-Based Sentiment Analysis (MABSA), a task that involves extracting aspect terms and sentiment polarities from text and images. LLMs like Llama2, LLaVA, and ChatGPT have shown strong capabilities in general tasks, but their performance in complex scenarios like MABSA is underexplored. The study constructs a benchmark to evaluate the performance of LLMs on MABSA tasks and compares them with state-of-the-art supervised learning methods. The results reveal that while LLMs demonstrate potential in multimodal understanding, they face significant challenges in achieving satisfactory results for MABSA, particularly in terms of accuracy and inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MABSA is a way to understand how people feel about certain things from text and images. Large language models are good at doing general tasks, but we don’t know if they’re good at this specific task yet. The researchers created a test to see how well these language models do on MABSA and compared them to other ways of doing the same thing. They found that while the language models can be good at some things, they still have trouble getting the right answers for MABSA. |
Keywords
» Artificial intelligence » Inference » Supervised
