4 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 3 cited
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation
Lasal Jayawardena, Prasan Yapa
Over the past year, the field of Natural Language Generation (NLG) has experienced an exponential surge, largely due to the introduction of Large Language Models (LLMs). These mode…
cs.CL2024★ 3 cited
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity
Lasal Jayawardena, Prasan Yapa
Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that cl…
cs.CL2024★ 4 cited
CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering
Nirmalie Wiratunga, Ramitha Abeyratne, Lasal Jayawardena +6
Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and exp…