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Found 3 papers, 2 papers with code
Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset
We also present experimental results and in-depth analysis using a new Chain-of-Thought prompting method, which asks LLMs to translate syllogisms into abstract logical expressions and then explain their reasoning process.
Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases
Our findings demonstrate that current large language models struggle more with problems involving these three types of biases.
Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention
We compared the proposed sensor-fusion method with strong baselines on the MMAC Captions dataset and found that using sensor data as supplementary information to the egocentric-video data was beneficial, and that our proposed method outperformed the strong baselines, demonstrating the effectiveness of the proposed method.
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