Summary of Textual Aesthetics in Large Language Models, by Lingjie Jiang et al.
Textual Aesthetics in Large Language Models
by Lingjie Jiang, Shaohan Huang, Xun Wu, Furu Wei
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper proposes a pipeline for enhancing the textual aesthetics of large language model (LLM) responses. While previous work focused on content correctness, this study highlights the importance of aesthetic appeal in LLM outputs. The authors introduce TexAes, a dataset designed to facilitate the development of textual aesthetics models. They also present TAPO, a fine-tuning method that optimizes LLMs for textual aesthetics without compromising content accuracy. Two evaluation methods are developed: one based on text analysis and another on image analysis. Experimental results demonstrate that using TexAes data and the TAPO approach improves aesthetic scores and enhances performance on general evaluation datasets such as AlpacalEval and Anera-hard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re having a conversation with a computer program, like Siri or Alexa. The program’s responses are helpful, but they might not be easy to read or understand. This paper is about making those responses look nicer and easier to follow. It proposes a new way of fine-tuning these programs so that their answers have better aesthetics, such as being more organized and coherent. The authors also create a dataset of examples to help train these models. They test their approach on several evaluation datasets and show that it improves the aesthetic appeal of the responses while still keeping them accurate. |
Keywords
» Artificial intelligence » Fine tuning » Large language model
