Zero-shot Conversational Summarization Evaluations with small Large Language Models

Large Language Models (LLMs) exhibit powerful summarization abilities. However, their capabilities on conversational summarization remains under explored. In this work we evaluate LLMs (approx. 10 billion parameters) on conversational summarization and showcase their performance on various prompts....

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Bibliographic Details
Published inarXiv.org
Main Authors Manuvinakurike, Ramesh, Sahay, Saurav, Manepalli, Sangeeta, Nachman, Lama
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.11.2023
Subjects
Large language models
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Summary:Large Language Models (LLMs) exhibit powerful summarization abilities. However, their capabilities on conversational summarization remains under explored. In this work we evaluate LLMs (approx. 10 billion parameters) on conversational summarization and showcase their performance on various prompts. We show that the summaries generated by models depend on the instructions and the performance of LLMs vary with different instructions sometimes resulting steep drop in ROUGE scores if prompts are not selected carefully. We also evaluate the models with human evaluations and discuss the limitations of the models on conversational summarization
ISSN:2331-8422