# Modeling Empathic Similarity in Personal Narratives arXiv: https://arxiv.org/abs/2305.14246 ยท HTML: https://mlsanity.com/paper/2305.14246/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 80 references that are in the map. - First posted on arXiv 23 May 2023. ## Builds on (most cited first, 22 of 22) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023) - [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019) - [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021) - [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020) - [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022) - [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020) - [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021) - [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://mlsanity.com/paper/2205.12688/) (2022) - [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023) - [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020) - [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018) - [Modeling Naive Psychology of Characters in Simple Commonsense Stories](https://mlsanity.com/paper/1805.06533/) (2018) - [Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations](https://mlsanity.com/paper/2102.08368/) (2021) - [Inside the Right-Leaning Echo Chambers: Characterizing Gab, an Unmoderated Social System](https://mlsanity.com/paper/1807.03688/) (2018) - [Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types](https://mlsanity.com/paper/2005.12762/) (2020) - [Mapping Between fMRI Responses to Movies and their Natural Language Annotations](https://mlsanity.com/paper/1610.03914/) (2016) - [Hatemongers ride on echo chambers to escalate hate speech diffusion](https://mlsanity.com/paper/2302.02479/) (2023) ## Built on by (most cited first, 14 of 14) - [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://mlsanity.com/paper/2405.17633/) (2024) - [EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences](https://mlsanity.com/paper/2405.15708/) (2024) - [Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting](https://mlsanity.com/paper/2305.14755/) (2023) - [Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods](https://mlsanity.com/paper/2311.00721/) (2023) - [Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey](https://mlsanity.com/paper/2602.15851/) (2026) - [Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication](https://mlsanity.com/paper/2505.21451/) (2025) - [MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform](https://mlsanity.com/paper/2506.00308/) (2025) - [Social Story Frames: Contextual Reasoning about Narrative Intent and Reception](https://mlsanity.com/paper/2512.15925/) (2025) - [Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs](https://mlsanity.com/paper/2406.11250/) (2024) - [Privacy-Preserving Empathy Detection in Video Interactions](https://mlsanity.com/paper/2504.10808/) (2025) - [UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression](https://mlsanity.com/paper/2508.03520/) (2025) - [Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives](https://mlsanity.com/paper/2604.20131/) (2026) - [Voice Under Revision: Large Language Models and the Normalization of Personal Narrative](https://mlsanity.com/paper/2604.22142/) (2026) - [Generating Place-Based Compromises Between Two Points of View](https://mlsanity.com/paper/2604.24536/) (2026) ## Abstract (arXiv) The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives. We introduce a new task of identifying similarity in personal stories based on empathic resonance, i.e., the extent to which two people empathize with each others' experiences, as opposed to raw semantic or lexical similarity, as has predominantly been studied in NLP. Using insights from social psychology, we craft a framework that operationalizes empathic similarity in terms of three key features of stories: main events, emotional trajectories, and overall morals or takeaways. We create EmpathicStories, a dataset of 1,500 personal stories annotated with our empathic similarity features, and 2,000 pairs of stories annotated with empathic similarity scores. Using our dataset, we fine-tune a model to compute empathic similarity of story pairs, and show that this outperforms semantic similarity models on automated correlation and retrieval metrics. Through a user study with 150 participants, we also assess the effect our model has on retrieving stories that users empathize with, compared to naive semantic similarity-based retrieval, and find that participants empathized significantly more with stories retrieved by our model. Our work has strong implications for the use of empathy-aware models to foster human connection and empathy between people.