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Found 8 papers, 4 papers with code
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT
We introduce a multi-stage prompting approach (MSP) for the generation of multiple choice questions (MCQs), harnessing the capabilities of GPT models such as text-davinci-003 and GPT-4, renowned for their excellence across various NLP tasks.
Multi-Label Classification of COVID-Tweets Using Large Language Models
Vaccination is important to minimize the risk and spread of various diseases.
Prompted Zero-Shot Multi-label Classification of Factual Incorrectness in Machine-Generated Summaries
This study addresses the critical issue of factual inaccuracies in machine-generated text summaries, an increasingly prevalent issue in information dissemination.
Harnessing the Power of Prompt-based Techniques for Generating School-Level Questions using Large Language Models
We carefully annotate this dataset as quadruples of 1) Context: a segment upon which the question is formed; 2) Long Prompt: a long textual cue for the question (i. e., a longer sequence of words or phrases, covering the main theme of the context); 3) Short Prompt: a short textual cue for the question (i. e., a condensed representation of the key information or focus of the context); 4) Question: a deep question that aligns with the context and is coherent with the prompts.
Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?
The evolution of legal datasets and the advent of large language models (LLMs) have significantly transformed the legal field, particularly in the generation of case judgment summaries.
Fact-based Court Judgment Prediction
This extended abstract extends the research presented in "ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation" \cite{malik-etal-2021-ildc}, focusing on fact-based judgment prediction within the context of Indian legal documents.
Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation
This paper describes our submission to the SemEval-2023 for Task 6 on LegalEval: Understanding Legal Texts.
How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?
In addition to standard metrics for summary quality, we check for inconsistencies and hallucinations in the summaries.
