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"Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning"

The podcast on this paper is generated with Google's Illuminate.

MT-ISA teaches smaller models to understand hidden sentiments by learning from LLM-generated insights

MT-ISA framework enhances implicit sentiment analysis by combining LLMs with multi-task learning, using automatic weight adjustments to handle uncertainties at data and task levels.

https://arxiv.org/abs/2412.09046

🤔 Original Problem:

→ Implicit sentiment analysis struggles with hidden opinions due to lack of clear sentiment words

→ Traditional models have limited reasoning capabilities and insufficient data for learning implicit patterns

→ LLMs can hallucinate, leading to unreliable sentiment analysis

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🔧 Solution in this Paper:

→ MT-ISA uses LLMs to generate auxiliary sentiment elements like aspects and opinions

→ The framework implements data-level automatic weight learning with three strategies: input scaling, output re-weighting, and combined approach

→ Task-level automatic weight learning uses homoscedastic uncertainty to balance primary and auxiliary tasks

→ Self-refining strategy with polarity intervention ensures reliable generation

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💡 Key Insights:

→ Different model sizes show distinct preferences - base models work better with input strategy, while larger models excel with output strategy

→ Automatic weight learning eliminates manual tuning and adapts to model capabilities

→ Combining data-level and task-level weight learning significantly improves performance

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📊 Results:

→ Achieves state-of-the-art performance on Restaurant14 and Laptop14 datasets

→ XXL model with output strategy reaches 92.68% accuracy on Restaurant14

→ Base model with input strategy achieves 82.91% accuracy on Laptop14

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