LLM-generated semantic descriptions of driving behaviors are encoded and used as privileged information exclusively during SVM+ training to improve alignment with expert driving style recognition while inference uses only sensor data.
Chain-of-thought prompting elicits reasoning in large language models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
MulFSA combines micro-level firm sentiment, meso-level industry sentiment, and duration-aware smoothing from PLMs/LLMs to extract a daily sentiment index that reduces credit spread forecast errors by 10.25% MAE and 11.94% MAPE on a 1.35M-text Chinese bond corpus.
Three-aspect RAG query pipeline optimization for cancer patient QA introduces HSRDR and SEOS and reports 5.24% accuracy gain on Claude-3-haiku versus chain-of-thought on a custom dataset.
citing papers explorer
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Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models
LLM-generated semantic descriptions of driving behaviors are encoded and used as privileged information exclusively during SVM+ training to improve alignment with expert driving style recognition while inference uses only sensor data.
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MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market
MulFSA combines micro-level firm sentiment, meso-level industry sentiment, and duration-aware smoothing from PLMs/LLMs to extract a daily sentiment index that reduces credit spread forecast errors by 10.25% MAE and 11.94% MAPE on a 1.35M-text Chinese bond corpus.
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Query pipeline optimization for cancer patient question answering systems
Three-aspect RAG query pipeline optimization for cancer patient QA introduces HSRDR and SEOS and reports 5.24% accuracy gain on Claude-3-haiku versus chain-of-thought on a custom dataset.