Summary of Measuring and Benchmarking Large Language Models’ Capabilities to Generate Persuasive Language, by Amalie Brogaard Pauli et al.
Measuring and Benchmarking Large Language Models’ Capabilities to Generate Persuasive Language
by Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A study explores the ability of Large Language Models (LLMs) to produce persuasive text across various domains. Unlike previous work focused on specific domains or types of persuasion, this research conducts a general study measuring and benchmarking LLMs’ persuasive language production when explicitly instructed or simply paraphrasing. The new dataset Persuasive-Pairs is constructed, featuring pairs of texts with their rewrites by an LLM to amplify or diminish persuasive language. The multi-annotated pairs are scored on a relative scale for persuasive language, creating a valuable resource for training regression models and benchmarking new LLMs across domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers investigated how well Large Language Models (LLMs) can create convincing writing. They looked at many types of persuasive language, not just one or two specific areas. The study found that even when the LLM was only asked to make minor changes to a piece of text, it could greatly change how persuasive the text was. This is important because it shows that these AI models are very good at understanding what makes writing persuasive and can use this skill in many different situations. |
Keywords
» Artificial intelligence » Regression
