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Found 7 papers, 3 papers with code
Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty
As natural language becomes the default interface for human-AI interaction, there is a critical need for LMs to appropriately communicate uncertainties in downstream applications.
Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
The increased deployment of LMs for real-world tasks involving knowledge and facts makes it important to understand model epistemology: what LMs think they know, and how their attitudes toward that knowledge are affected by language use in their inputs.
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult.
Richer Countries and Richer Representations
We examine whether some countries are more richly represented in embedding space than others.
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
Cosine similarity of contextual embeddings is used in many NLP tasks (e. g., QA, IR, MT) and metrics (e. g., BERTScore).
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Frequency-based Distortions in Contextualized Word Embeddings
How does word frequency in pre-training data affect the behavior of similarity metrics in contextualized BERT embeddings?
