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Found 6 papers, 5 papers with code
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words
The performance of sentence encoders can be significantly improved through the simple practice of fine-tuning using contrastive loss.
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e. g., chain-of-thought (CoT) prompting.
Transformer Language Models Handle Word Frequency in Prediction Head
Prediction head is a crucial component of Transformer language models.
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
Transformers are ubiquitous in wide tasks.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Transformer architecture has become ubiquitous in the natural language processing field.
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing.
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