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Found 21 papers, 7 papers with code
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation
Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts.
A State-Vector Framework for Dataset Effects
The impressive success of recent deep neural network (DNN)-based systems is significantly influenced by the high-quality datasets used in training.
Measuring Information in Text Explanations
Text-based explanation is a particularly promising approach in explainable AI, but the evaluation of text explanations is method-dependent.
Situated Natural Language Explanations
Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
CCGen: Explainable Complementary Concept Generation in E-Commerce
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e. g., "Digital Cameras", generating a list of complementary concepts, e. g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers.
Predicting Fine-Tuning Performance with Probing
Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance.
OOD-Probe: A Neural Interpretation of Out-of-Domain Generalization
We find that representations always encode some information about the domain.
On the data requirements of probing
As large and powerful neural language models are developed, researchers have been increasingly interested in developing diagnostic tools to probe them.
Neural reality of argument structure constructions
Second, in a "Jabberwocky" priming-based experiment, we find that LMs associate ASCs with meaning, even in semantically nonsensical sentences.
Quantifying the Task-Specific Information in Text-Based Classifications
This framework allows us to compare across datasets, saying that, apart from a set of ``shortcut features'', classifying each sample in the Multi-NLI task involves around 0. 4 nats more TSI than in the Quora Question Pair.
An unsupervised framework for tracing textual sources of moral change
Morality plays an important role in social well-being, but people's moral perception is not stable and changes over time.
What do writing features tell us about AI papers?
As the numbers of submissions to conferences grow quickly, the task of assessing the quality of academic papers automatically, convincingly, and with high accuracy attracts increasing attention.
How is BERT surprised? Layerwise detection of linguistic anomalies
Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly.
Semantic coordinates analysis reveals language changes in the AI field
Semantic shifts can reflect changes in beliefs across hundreds of years, but it is less clear whether trends in fast-changing communities across a short time can be detected.
Examining the rhetorical capacities of neural language models
In this paper, we propose a method that quantitatively evaluates the rhetorical capacities of neural LMs.
An information theoretic view on selecting linguistic probes
Hewitt and Liang (2019) showed that a high performance on diagnostic classification itself is insufficient, because it can be attributed to either "the representation being rich in knowledge", or "the probe learning the task", which Pimentel et al. (2020) challenged.
Robustness against the channel effect in pathological voice detection
In this study, we propose a detection system for pathological voice, which is robust against the channel effect.
Detecting cognitive impairments by agreeing on interpretations of linguistic features
In this paper, we also present two methods that improve the performance of CNs.
Deconfounding age effects with fair representation learning when assessing dementia
One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts.
Semi-supervised classification by reaching consensus among modalities
Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data.
Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors
Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making.
