Search Results for author:
Found 19 papers, 8 papers with code
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.
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.
Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerpts
To address this, we explore how Large Language Models (LLMs) can enrich these excerpts by providing socially relevant context.
Bridging the Data Provenance Gap Across Text, Speech and Video
In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities--popular text, speech, and video datasets--from their detailed sourcing trends and use restrictions to their geographical and linguistic representation.
On the Relationship between Truth and Political Bias in Language Models
In this work, we focus on analyzing the relationship between two concepts essential in both language model alignment and political science: truthfulness and political bias.
Consent in Crisis: The Rapid Decline of the AI Data Commons
To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora.
LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
While state-of-the-art Large Language Models (LLMs) have shown impressive performance on many tasks, there has been extensive research on undesirable model behavior such as hallucinations and bias.
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models
Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models' internal and expressed confidence.
Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections.
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.
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.
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
Learning on text-attributed graphs (TAGs), in which nodes are associated with one or more texts, has been the subject of much recent work.
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.
