A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
D aily D ialog: A Manually Labelled Multi-turn Dialogue Dataset
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Authors build an emotional intensity dataset and fine-tune generative LLMs to predict continuous 0-100 scores, claiming outperformance over classification baselines plus generalization to sentiment and arousal.
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Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
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Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Text
Authors build an emotional intensity dataset and fine-tune generative LLMs to predict continuous 0-100 scores, claiming outperformance over classification baselines plus generalization to sentiment and arousal.