A difficulty-aware conversational knowledge tracing framework that combines LLMs with Item Response Theory to produce interpretable student performance predictions in tutor dialogues.
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CAP is a reinforcement-learning-driven prompt optimization framework that suppresses target knowledge in LLMs while preserving general capabilities, enabling reversible unlearning without any parameter updates.
LLMs prompted with few-shot examples and rationales generate better reasoned distractors for MCQs than fine-tuned contrastive models across six benchmarks.
PixArt-α matches commercial text-to-image quality with a diffusion transformer trained in 675 A100 GPU days through decomposed training stages, cross-attention text injection, and vision-language model dense captions.
Cosine similarity poorly predicts performance degradation from layer removal in LLMs, making direct accuracy-drop ablation a more reliable relevance metric.
Lightweight LLMs are benchmarked for court view generation and charge prediction across architectures, sizes, DNN comparisons, and task ordering on three datasets using the new CVGEvalKit framework.
citing papers explorer
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Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
A difficulty-aware conversational knowledge tracing framework that combines LLMs with Item Response Theory to produce interpretable student performance predictions in tutor dialogues.
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CAP: Controllable Alignment Prompting for Unlearning in LLMs
CAP is a reinforcement-learning-driven prompt optimization framework that suppresses target knowledge in LLMs while preserving general capabilities, enabling reversible unlearning without any parameter updates.
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Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
LLMs prompted with few-shot examples and rationales generate better reasoned distractors for MCQs than fine-tuned contrastive models across six benchmarks.
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PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
PixArt-α matches commercial text-to-image quality with a diffusion transformer trained in 675 A100 GPU days through decomposed training stages, cross-attention text injection, and vision-language model dense captions.
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Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity
Cosine similarity poorly predicts performance degradation from layer removal in LLMs, making direct accuracy-drop ablation a more reliable relevance metric.
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Exploring Lightweight Large Language Models for Court View Generation
Lightweight LLMs are benchmarked for court view generation and charge prediction across architectures, sizes, DNN comparisons, and task ordering on three datasets using the new CVGEvalKit framework.