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Narrative detection is an important task across diverse research domains where storytelling serves as a key mechanism for explaining human beliefs and behavior. However, the task faces three significant challenges: (1) inter-narrative heterogeneity, or the variation in narrative communication across social contexts; (2) intra-narrative heterogeneity, or the dynamic variation of narrative features within a single text over time; and (3) the lack of theoretical consensus regarding the concept of narrative. This paper introduces the NarraDetect dataset, a comprehensive resource comprising over 13,000 passages from 18 distinct narrative and non-narrative genres. Through a manually annotated subset of ~400 passages, we also introduce a novel theoretical framework for annotating for a scalar concept of &quot;narrativity.&#39;&#39; Our findings indicate that while supervised models outperform large language models (LLMs) on this dataset, LLMs exhibit stronger generalization and alignment with the scalar concept of narrativity.

NAACL 2025

NarraDetect: An annotated dataset for the task of narrative detection

narrativity

narrative detection

narratives

datasets

theory

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Narrative detection is an important task across diverse research domains where storytelling serves as a key mechanism for explaining human beliefs and behavior. However, the task faces three significant challenges: (1) inter-narrative heterogeneity, or the variation in narrative communication across social contexts; (2) intra-narrative heterogeneity, or the dynamic variation of narrative features within a single text over time; and (3) the lack of theoretical consensus regarding the concept of narrative. This paper introduces the NarraDetect dataset, a comprehensive resource comprising over 13,000 passages from 18 distinct narrative and non-narrative genres. Through a manually annotated subset of ~400 passages, we also introduce a novel theoretical framework for annotating for a scalar concept of "narrativity.'' Our findings indicate that while supervised models outperform large language models (LLMs) on this dataset, LLMs exhibit stronger generalization and alignment with the scalar concept of narrativity.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

How do emotional progressions shape narrative impact? In this paper, we introduce a new taxonomy of short-form story types based on specific emotional progressions across each half of the text, or "emotion sequences." Using transformer-based classifiers, we analyze over 14,000 medical fundraising pitches from GoFundMe.org and estimate the impact of particular emotion sequences on fundraising success. Among other findings, we show that medical fundraising pitches that begin with a sad tone and end on a caring tone are significantly more likely to succeed. We then develop a novel framework for testing the generalizability of our field findings using crowd-sourced, LLM-assisted rewrites to introduce these emotion sequences to randomly-selected fundraisers. We demonstrate that, while solely-human rewrites often fail due to skill deficits, LLM-assisted rewrites allow researchers to effectively enact and test the out-of-sample application of research results using online participants. With this approach, we establish that pitches rewritten to feature our focal emotion sequences see a significant boost in perceived quality, even for some sequences associated with lower success in observational data, while placebo rewrites produce null effects. This methodology provides a scalable and generalizable framework for testing causal effects of linguistic phenomena in naturalistic settings.

Emotion Sequences and Persuasive Stories: Evidence from Online Fundraising and LLM-Assisted Rewrites

Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication. Previous LLM-based researches mainly focus on the multi-agent framework, while their base LLMs are still pairwisely fine-tuned. In this work, we design a multi-party fine-tuning framework (MuPaS) for LLMs on the multi-party dialogue datasets, and prove such a straightforward framework can let the LLM align with the multi-party conversation style efficiently and effectively. We also design two training strategies which can convert MuPaS into the MPD simulator. Substantial experiments show that MuPaS can achieve state-of-the-art multi-party response, higher accuracy of the-next-speaker prediction, higher human and automatic evaluated utterance qualities, and can even generate reasonably with the out-of-distribution scene, topic and role descriptions. The MuPaS framework bridges the LLM training with more complicated multi-party applications, such as conversation generation, virtual rehearsal or meta-universe.

Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation

We present an interesting application of narrative understanding in the clinical assessment of aphasia, where story retelling tasks are used to evaluate a patient's communication abilities. This clinical setting provides a framework to help operationalize narrative discourse analysis and an application-focused evaluation method for narrative understanding systems. In particular, we highlight the use of main concepts (MCs)---a list of statements that capture a story's gist---for aphasic discourse analysis. We then propose the task of automatically generating MCs from novel stories, that can be edited manually by experts thus enabling wider adaptation of current assessment tools. We further develop a prompt ensemble method using large language models (LLMs) to automatically generate MCs for a given story. We evaluate our proposed method on an existing narrative summarization dataset to establish its intrinsic validity. We further apply it to a set of stories that have been annotated with MCs through extensive analysis of retells from non-aphasic and aphasic participants (Kurland et al., 2021). Our results show that our proposed method can generate most of the gold-standard MCs for stories from this dataset. Finally, we release this dataset of stories with annotated MCs, to spur more research in this area.

Automated main concept generation for narrative discourse assessment in aphasia

Domestic violence survivors often share their experiences in online spaces, offering valuable insights into common abuse patterns. This study analyzes a dataset of personal narratives about domestic violence from Reddit, focusing on event extraction and topic modeling to uncover recurring themes. We evaluate GPT-4 and LLaMA-3.1 for extracting key sentences, finding that GPT-4 exhibits higher precision, while LLaMA-3.1 achieves better recall. Using LLM-based topic assignment, we identify dominant themes such as psychological aggression, financial abuse, and physical assault which align with previously published psychology findings. A co-occurrence and PMI analysis further reveals the interdependencies among different abuse types, emphasizing the multifaceted nature of domestic violence. Our findings provide a structured approach to analyzing survivor narratives, with implications for social support systems and policy interventions.

Finding Common Patterns in Domestic Violence Stories Posted on Reddit

Narrative surprise is a core element of storytelling for engaging audiences, and yet it remains underexplored in the context of large language models (LLMs) and narrative generation. While surprise arises from events that deviate from expectations while maintaining retrospective coherence, current computational approaches lack comprehensive frameworks to evaluate this phenomenon. This paper presents a novel framework for assessing narrative surprise, drawing on psychological theories of narrative comprehension and surprise intensity. We operationalize six criteria—initiatoriness, immutability violation, predictability, post-dictability, importance, and valence—to measure narrative surprise in story endings. Our study evaluates 120 story endings, generated by both human authors and LLMs, across 30 mystery narratives. Through a ranked-choice voting methodology, we identify significant correlations between reader preferences and four of the six criteria. Results underscore the continuing advantage of human-authored endings in achieving compelling narrative surprise, while also revealing significant progress in LLM-generated narratives.

A Theoretical Framework for Evaluating Narrative Surprise in Large Language Models

Public Narratives (PNs) are central to leadership development and civic mobilization, yet their systematic analysis remains challenging due to the subjective nature of narrative elements and the high cost of expert annotation. In this work, we propose a novel computational framework that leverages large language models (LLMs) to automate the qualitative annotation of public narratives. Building on a rigorously developed codebook—co-created with subject-matter experts—we evaluate LLM performance against expert annotations. Our experiments, which compare multiple prompting techniques, reveal that while LLMs achieve near-human performance on explicit, personal narrative elements, they face challenges with more nuanced, collective, and forward-looking components. This study not only demonstrates the potential of LLM-assisted annotation for scalable narrative analysis but also highlights key limitations and directions for future research in computational civic storytelling.

LLMs for Public Narrative Understanding

Computational narrative understanding studies the identification, description, and interaction of the elements of a narrative: characters, attributes, events, and relations. Narrative research has given considerable attention to defining and classifying character types. However, these character-type taxonomies do not generalize well because they are small, too simple, or specific to a domain. We require robust and reliable benchmarks to test whether narrative models truly understand the nuances of the character's development in the story. Our work addresses this by curating the CHATTER dataset that labels whether a character portrays some attribute for 88124 character-attribute pairs, encompassing 2998 characters, 12967 attributes and 660 movies. We validate a subset of CHATTER, called CHATTEREVAL, using human annotations to serve as an evaluation benchmark for the character attribution task in movie scripts. CHATTEREVAL also assesses narrative understanding and the long-context modeling capacity of language models.

CHATTER: A character-attribution dataset for narrative understanding

This work aims to assess the zero-shot social reasoning capabilities of LLMs by proposing various strategies based on the granularity of information used to track the fine-grained evolution in the relationship between characters in a book. Without gold annotations, we thoroughly analyze the agreements between predictions from multiple LLMs and manually examine their consensus at a local and global level via the task of trope prediction. Our findings reveal low-to-moderate agreement among LLMs and humans, reflecting the complexity of the task. Analysis shows that LLMs are sensitive to subtle contextual changes and often rely on surface-level cues. Humans, too, may interpret relationships differently, leading to disagreements in annotations.

Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models

While Large Language Models (LLMs) have been shown to excel at various natural language understanding and reasoning problems, human-level deeper understanding of long-form narratives and stories remains a challenge. In this work, we consider the problem of detecting plot holes in stories: story inconsistencies that goes against the flow of logic established by its plot. We propose an algorithm--PLOTHOLEMAKER-- that creates a high-quality and contamination-free benchmark for this problem by synthesizing plot holes in existing stories. We find that state-of-the-art reasoning models struggle in accurately solving our synthesized task, PLOTHOLEBENCH, that includes careful human-quality filtering. We also observe that scaling the number of reasoning tokens results in little to no improvement on the performance of these models on our benchmark and their performance becomes substantially worse when considering longer stories.

Evaluating LLMs' Ability to Reason about Impossible Worlds

Speaker identification in narrative analysis is a challenging task due to complex dialogues, diverse utterance patterns, and ambiguous character references. Cosly and time-intensive manual annotation limits the scalability of high-quality dataset creation. This study demonstrates a cost-efficient approach of constructing speaker identification datasets by combining small-scale manual annotation with LLM-based labeling. A subset of data is manually annotated and is used to guide LLM predictions with a few-shot approach followed by refinement through minimal human corrections. Our results show that LLMs achieve approximately 90% accuracy on challenging narratives, such as the "Three Kingdoms'' dataset, underscoring the importance of targeted human corrections. This approach proves effective for constructing scalable and cost-efficient datasets for Japanese and complex narratives.

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Emotion Sequences and Persuasive Stories: Evidence from Online Fundraising and LLM-Assisted Rewrites

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