Summary of Show, Don’t Tell: Uncovering Implicit Character Portrayal Using Llms, by Brandon Jaipersaud et al.
Show, Don’t Tell: Uncovering Implicit Character Portrayal using LLMs
by Brandon Jaipersaud, Zining Zhu, Frank Rudzicz, Elliot Creager
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
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 new framework called LIIPA uses large language models (LLMs) to uncover implicit character portrayals in fiction, which can be valuable for writers and literary scholars. The existing tools rely on explicit textual indicators of character attributes, but this framework addresses the gap by leveraging LLMs to reveal implicit portrayal through actions and behaviors. A dataset was generated with greater cross-topic similarity, lexical diversity, and narrative lengths than existing corpora. The LIIPA framework can be configured to use various types of intermediate computation to infer how fictional characters are portrayed in the source text. Results show that LIIPA outperforms existing approaches and is more robust to increasing character counts due to its ability to utilize full narrative context. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary LIIPA uses large language models (LLMs) to uncover implicit character portrayals in fiction, making it easier for writers and scholars to understand characters. The LLMs look at the actions and behaviors of characters instead of just what is explicitly said about them. A special dataset was created with lots of different stories to help train the LLMs. The framework can be adjusted to use different ways of understanding how characters are portrayed in a story. It works better than other methods when there are more characters in the story and it uses all the information from the story. |