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Found 12 papers, 5 papers with code
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences
Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of language.
Post-Editing Extractive Summaries by Definiteness Prediction
Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries.
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment.
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners.
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes.
Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models
Debiasing methods that seek to mitigate the tendency of Language Models (LMs) to occasionally output toxic or inappropriate text have recently gained traction.
PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits.
Computational Investigations of Pragmatic Effects in Natural Language
Semantics and pragmatics are two complimentary and intertwined aspects of meaning in language.
Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers
We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems.
Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers
We introduce the task of predicting adverbial presupposition triggers such as also and again.
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs
We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction.
