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Found 9 papers, 5 papers with code
“What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation
We propose a type-controlled framework for inquisitive question generation.
Ambiguity-Aware In-Context Learning with Large Language Models
In-context learning (ICL) i. e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required.
ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind
In addition, our paper wants to raise awareness in evaluating the ToM in LLMs and we want to invite more discussion on how to design the prompts and tasks for ToM tasks that can better assess the LLMs' ability.
The Benefits of Label-Description Training for Zero-Shot Text Classification
Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific label sets in downstream tasks.
Evaluating Transformer Models and Human Behaviors on Chinese Character Naming
These results suggested that the transformer models can well capture human's character naming behavior.
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection
The different behaviors on the regulars and irregulars suggest that the models have some degree of symbolic learning on the regularity of the verbs.
"What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation
We propose a type-controlled framework for inquisitive question generation.
Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners
Simple features of the distractor and correct answer correlate with the annotations, though we find substantial benefit to additionally using large-scale pretrained models to measure the fit of the distractor in the context.
A Cross-Task Analysis of Text Span Representations
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution.
