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Found 3 papers, 1 papers with code
Clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents
Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents"-agents that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings.
EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts
To the best of our knowledge, this work presents the first lightweight deep learning approach for smartphone deployment of emphasis selection.
LiteMuL: A Lightweight On-Device Sequence Tagger using Multi-task Learning
The proposed LiteMuL not only outperforms the current state of the art results but also surpasses the results of our proposed on-device task-specific models, with accuracy gains of up to 11% and model-size reduction by 50%-56%.
