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Research Paper#Language Models, Cognitive Science🔬 ResearchAnalyzed: Jan 3, 2026 18:31

Context Reduction in Language Model Probabilities

Published:Dec 29, 2025 18:12
1 min read
ArXiv

Analysis

This paper investigates the minimal context required to observe probabilistic reduction in language models, a phenomenon relevant to cognitive science. It challenges the assumption that whole utterances are necessary, suggesting that n-gram representations are sufficient. This has implications for understanding how language models relate to human cognitive processes and could lead to more efficient model analysis.
Reference

“n-gram representations suffice as cognitive units of planning.”

PermalinkArXiv
Research Paper#Natural Language Processing, Sentiment Analysis, Emotion Detection, Nepali Language, Reddit🔬 ResearchAnalyzed: Jan 3, 2026 16:20

NepEMO: Emotion and Sentiment Analysis on Nepali Reddit

Published:Dec 28, 2025 07:27
1 min read
ArXiv

Analysis

This paper addresses a gap in NLP research by focusing on Nepali language and culture, specifically analyzing emotions and sentiment on Reddit. The creation of a new dataset (NepEMO) is a significant contribution, enabling further research in this area. The paper's analysis of linguistic insights and comparison of various models provides valuable information for researchers and practitioners interested in Nepali NLP.
Reference

“Transformer models consistently outperform the ML and DL models for both MLE and SC tasks.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:17

Beyond N-grams: A Hierarchical Reward Learning Framework for Clinically-Aware Medical Report Generation

Published:Dec 2, 2025 12:41
1 min read
ArXiv

Analysis

This article introduces a new framework for generating medical reports using AI. The focus is on moving beyond traditional N-gram models and incorporating a hierarchical reward learning approach to improve the clinical relevance and accuracy of the generated reports. The use of 'clinically-aware' suggests an emphasis on the practical application and impact of the AI in a medical context.
Reference

“”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 29, 2025 09:36

Boosting Wav2Vec2 with n-grams in 🤗 Transformers

Published:Jan 12, 2022 00:00
1 min read
Hugging Face

Analysis

This article likely discusses a method to improve the performance of the Wav2Vec2 model, a popular speech recognition model, by incorporating n-grams. N-grams, sequences of n words, are used to model word dependencies and improve the accuracy of speech-to-text tasks. The use of the Hugging Face Transformers library suggests the implementation is accessible and potentially easy to integrate. The article probably details the technical aspects of the implementation, including how n-grams are integrated into the Wav2Vec2 architecture and the performance gains achieved.
Reference

“The article likely includes a quote from a researcher or developer involved in the project, possibly highlighting the benefits of using n-grams or the ease of implementation with the Transformers library.”

PermalinkHugging Face