Summary of Multi-task Learning with Llms For Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning, by Wenna Lai et al.
Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning
by Wenna Lai, Haoran Xie, Guandong Xu, Qing Li
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 proposed MT-ISA framework integrates multi-task learning with large language models to improve implicit sentiment analysis. The novel approach addresses data-level uncertainty from hallucination problems and task-level uncertainty from varying model capacities by leveraging generation and reasoning capabilities. MT-ISA constructs auxiliary tasks using generative LLMs, incorporates automatic MTL, and introduces data-level and task-level automatic weight learning. This enables models of varying sizes to adaptively learn fine-grained weights based on their reasoning capabilities. The framework achieves an optimal balance between primary prediction and auxiliary tasks in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Implicit sentiment analysis is a challenging task that requires machines to understand human opinions without explicit cues. Previous methods struggled due to limited data and reasoning capabilities. A new approach combines large language models with multi-task learning to improve opinion recognition. This method uses generative language models to create additional tasks and learns how to prioritize information based on the model’s abilities. The results show that this method can help machines of different sizes work together effectively. |
Keywords
» Artificial intelligence » Hallucination » Multi task
