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Found 19 papers, 6 papers with code
Active Learning for BERT: An Empirical Study
Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets.
Multi-Domain Explainability of Preferences
In this work, we propose a fully automated end-to-end method for generating local and global concept-based explanations of preferences across multiple domains.
WildIFEval: Instruction Following in the Wild
In this work, we introduce WildIFEval - a large-scale dataset of 12K real user instructions with diverse, multi-constraint conditions.
Conversational Prompt Engineering
We propose Conversational Prompt Engineering (CPE), a user-friendly tool that helps users create personalized prompts for their specific tasks.
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications
Here, we provide recommended HP configurations for practical use-cases that represent a better starting point for practitioners, when considering two SOTA LLMs and two commonly used tuning methods.
Label-Efficient Model Selection for Text Generation
Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models.
Efficient Benchmarking of Language Models
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.
Active Learning for Natural Language Generation
In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model.
Zero-Shot Text Classification with Self-Training
Recent advances in large pretrained language models have increased attention to zero-shot text classification.
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
Diversity Enhanced Table-to-Text Generation via Type Control
Generating natural language statements to convey logical inferences from tabular data (i. e., Logical NLG) is a process with one input and a variety of valid outputs.
Quality Controlled Paraphrase Generation
Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks.
Advances in Debating Technologies: Building AI That Can Debate Humans
We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components.
Financial Event Extraction Using Wikipedia-Based Weak Supervision
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
Fast End-to-End Wikification
Wikification of large corpora is beneficial for various NLP applications.
