@inproceedings{chen-si-2024-reflections-resonance,
title = "Reflections {\&} Resonance: Two-Agent Partnership for Advancing {LLM}-based Story Annotation",
author = "Chen, Yuetian and
Si, Mei",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1206",
pages = "13813--13818",
abstract = "We introduce a novel multi-agent system for automating story annotation through the generation of tailored prompts for a large language model (LLM). This system utilizes two agents: Agent A is responsible for generating prompts that identify the key information necessary for reconstructing the story, while Agent B reconstructs the story from these annotations and provides feedback to refine the initial prompts. Human evaluations and perplexity scores revealed that optimized prompts significantly enhance the model{'}s narrative reconstruction accuracy and confidence, demonstrating that dynamic interaction between agents substantially boosts the annotation process{'}s precision and efficiency. Utilizing this innovative approach, we created the {``}StorySense{''} corpus, containing 615 stories, meticulously annotated to facilitate comprehensive story analysis. The paper also demonstrates the practical application of our annotated dataset by drawing the story arcs of two distinct stories, showcasing the utility of the annotated information in story structure analysis and understanding.",
}
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<abstract>We introduce a novel multi-agent system for automating story annotation through the generation of tailored prompts for a large language model (LLM). This system utilizes two agents: Agent A is responsible for generating prompts that identify the key information necessary for reconstructing the story, while Agent B reconstructs the story from these annotations and provides feedback to refine the initial prompts. Human evaluations and perplexity scores revealed that optimized prompts significantly enhance the model’s narrative reconstruction accuracy and confidence, demonstrating that dynamic interaction between agents substantially boosts the annotation process’s precision and efficiency. Utilizing this innovative approach, we created the “StorySense” corpus, containing 615 stories, meticulously annotated to facilitate comprehensive story analysis. The paper also demonstrates the practical application of our annotated dataset by drawing the story arcs of two distinct stories, showcasing the utility of the annotated information in story structure analysis and understanding.</abstract>
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%0 Conference Proceedings
%T Reflections & Resonance: Two-Agent Partnership for Advancing LLM-based Story Annotation
%A Chen, Yuetian
%A Si, Mei
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F chen-si-2024-reflections-resonance
%X We introduce a novel multi-agent system for automating story annotation through the generation of tailored prompts for a large language model (LLM). This system utilizes two agents: Agent A is responsible for generating prompts that identify the key information necessary for reconstructing the story, while Agent B reconstructs the story from these annotations and provides feedback to refine the initial prompts. Human evaluations and perplexity scores revealed that optimized prompts significantly enhance the model’s narrative reconstruction accuracy and confidence, demonstrating that dynamic interaction between agents substantially boosts the annotation process’s precision and efficiency. Utilizing this innovative approach, we created the “StorySense” corpus, containing 615 stories, meticulously annotated to facilitate comprehensive story analysis. The paper also demonstrates the practical application of our annotated dataset by drawing the story arcs of two distinct stories, showcasing the utility of the annotated information in story structure analysis and understanding.
%U https://aclanthology.org/2024.lrec-main.1206
%P 13813-13818
Markdown (Informal)
[Reflections & Resonance: Two-Agent Partnership for Advancing LLM-based Story Annotation](https://aclanthology.org/2024.lrec-main.1206) (Chen & Si, LREC-COLING 2024)
ACL