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Found 6 papers, 3 papers with code
LeanReasoner: Boosting Complex Logical Reasoning with Lean
Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning.
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?
Also, we show that we can improve the controllability of LLMs with keyword-based classifier-free guidance (CFG) while achieving lexical overlap comparable to strong fine-tuned baselines on arXiv and PubMed.
Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains
Although large language models (LLMs) exhibit remarkable capacity to leverage in-context demonstrations, it is still unclear to what extent they can learn new concepts or facts from ground-truth labels.
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers.
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Learning to predict visual brain activity by predicting future sensory states
Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states.
Unsupervised predictive coding models may explain visual brain representation
Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states.
