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Found 4 papers, 2 papers with code
Semantic Content Prediction for Generating Interviewing Dialogues to Elicit Users’ Food Preferences
Next, using the corpus as training data, we created a classification model for the communicative function of the interviewer’s next utterance and a generative model that predicts the semantic content of the utterance based on the dialogue history.
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i. e. Complex Instructions Following).
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
Polymerized Feature-based Domain Adaptation for Cervical Cancer Dose Map Prediction
Recently, deep learning (DL) has automated and accelerated the clinical radiation therapy (RT) planning significantly by predicting accurate dose maps.
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