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Found 11 papers, 4 papers with code
NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research.
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
Reasoning is a fundamental component of language understanding.
Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness
Effective out-of-distribution (OOD) detection is crucial for reliable machine learning models, yet most current methods are limited in practical use due to requirements like access to training data or intervention in training.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration
In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt.
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods
By connecting our findings to instance categories based on training dynamics, we show that the agreement of saliency method explanations is very low for easy-to-learn instances.
Staying True to Your Word: (How) Can Attention Become Explanation?
The attention mechanism has quickly become ubiquitous in NLP.
Iterative Recursive Attention Model for Interpretable Sequence Classification
Natural language processing has greatly benefited from the introduction of the attention mechanism.
Predicting News Values from Headline Text and Emotions
We present a preliminary study on predicting news values from headline text and emotions.
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0. 733.
Two Layers of Annotation for Representing Event Mentions in News Stories
In this paper, we describe our preliminary study on annotating event mention as a part of our research on high-precision news event extraction models.
