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Found 12 papers, 2 papers with code
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.
VIRATrustData: A Trust-Annotated Corpus of Human-Chatbot Conversations About COVID-19 Vaccines
Public trust in medical information is crucial for successful application of public health policies such as vaccine uptake.
Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
We use this framework to report baseline intent discovery results over VIRADialogs, that highlight the difficulty of this task.
The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation
Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information.
What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus
One of the most impressive human endeavors of the past two decades is the collection and categorization of human knowledge in the free and accessible format that is Wikipedia.
A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis
To this end, we created a corpus of 30, 497 arguments carefully annotated for point-wise quality, released as part of this work.
Automatic Argument Quality Assessment - New Datasets and Methods
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
Automatic Argument Quality Assessment -- New Datasets and Methods
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining
To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech.
Towards an argumentative content search engine using weak supervision
Our results clearly indicate that the system is able to successfully generalize from the weak signal, outperforming previously reported results in terms of both precision and coverage.
Unsupervised corpus--wide claim detection
Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration.
