EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences (2024)

Abstract: Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often being confined to in-lab or acted scenarios, lacking longitudinal data, and missing self-reported labels. We introduce a new multimodal dataset for empathy during personal experience sharing: the EMPATHICSTORIES++ dataset1 containing 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. EMPATHICSTORIES++ is the first longitudinal dataset on empathy, collected over a month-long deployment of social robots in participants’ homes, as participants engage in natural, empathic storytelling interactions with AI agents. We then introduce a novel task of predicting individuals’ empathy toward others’ stories based on their personal experiences, evaluated in two contexts: participants’ own personal shared story context and their reflections https://mitmedialab.github.io/ empathic-stories-multimodal/ on stories they read. We benchmark this task using state-of-the-art models to pave the way for future improvements in contextualized and longitudinal empathy modeling. Our work provides a valuable resource for further research in developing empathetic AI systems and understanding the intricacies of human empathy within genuine, real-world settings.

text: Findings of the Association for Computational Linguistics ACL 2024, pages 4525–4536 August 11-16, 2024 ©2024 Association for Computational Linguistics
1https://mitmedialab.github.io/ empathic-stories-multimodal/
on stories they read. We benchmark this task using state-of-the-art models to pave the way for future improvements in contextualized and longitudinal empathy modeling. Our work provides a valuable resource for further research in developing empathetic AI systems and understanding the intricacies of human empathy within genuine, real-world settings.

heading: 1 Introduction
text: Empathy is a fundamental pillar of interpersonal human interactions ranging from prosocial behavior to enhancing human connection (Morelli et al., 2015). Modeling and understanding empathy is a complex task, due to its inherently interpersonal and experiential nature: empathy is tied to neurological synchronizations between representations of self and other (Decety and Lamm), and is dependent on a person’s past experiences (Hodges et al., 2010). Interest in empathy within AI communities has grown in recent years, as systems advance in context-awareness, naturalness, and fluency, although they typically fall short in social reasoning (Sap et al., 2022). Few prior works present datasets that are sufficient to capture the richness
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of human empathy responses during personal experience sharing. These datasets are limited in the following ways: (1) They are not captured inthe-wild. Existing multimodal empathy datasets are sourced from in-lab, online, or acted settings, which may differ greatly from empathy expressed in natural conversations. (2) They are not longitudinal, capturing only one-shot interaction settings, despite empathy being dependent on a combination of many past experiences. (3) Previous datasets are not self-labeled. While empathy can be inferred by external cues, it is an inherently subjective process, requiring self-reported labels for user-centric or personalized modeling.
In this work, we present the EMPATHICSTORIES++ dataset, a multimodal dataset collected from a month-long deployment of social robots inthe-wild. In this work, participants shared personal stories with the robot, read stories that were empathically similar to their own experience, and then reflected on stories they empathized with (Shen et al., 2023a). Our interaction design allows researchers to explore empathy in the context of personal experience sharing and understand the influence of users’ past experiences on their empathy towards others’ experiences. We address gaps in previous empathy and emotion recognition datasets through the following attributes: (1) Participant data is captured in their own homes with a social robot. Previous works have shown that users are more comfortable disclosing sensitive information with AI partners than with people (Pickard et al., 2016; Lucas et al., 2014). Participants in our study shared emotionally diverse and vulnerable stories from the comfort of their own homes. (2) Participants interacted with the robot over the course of a month, allowing us to obtain longitudinal data. (3) After participants read other peoples’ empathically similar stories, they self-rated their empathy towards the story, resulting in more authentic empathy labels. In addition to providing the raw video, audio, and text data for each interaction, we provide extracted features from all three modalities, as well as self-reported psychometric data (i.e. personality, well-being, etc.) and empathy ratings towards other people’s stories. These properties enable AI researchers to capture the complexity of empathy in its contextual, longitudinal, and personal dimensions. In addition to providing the EMPATHICSTORIES++ dataset, we present a new task on predicting a person’s empathy towards other’s
stories based on their own personal experiences. We evaluate this task in two settings: (1) predicting empathy based on the user’s own shared story (e.g. “I do weekly pickups at the local grocery store to bring leftover food to the food banks...”), and (2) predicting empathy based on a user’s reflection on a story they read (e.g. “I can really relate to the narrator’s feeling of wanting to help others...”). In summary, our contributions are as follows: (1) The first multimodal dataset with in-the-wild, longterm, and self-reported cues on empathy towards other people’s experiences, containing video, audio, text, as well as low-level features from each modality, self-reported psychometric data, and empathy ratings towards other people’s stories. (2) A novel task for predicting a user’s empathy towards another person’s story. (3) Benchmarking empathy prediction using state-of-the-art approaches to enable further improvements in contextual and longitudinal empathy modeling. Our work is a valuable resource for future work in developing socialemotional AI systems, improving interpretability of empathy prediction models, and promoting research on understanding cognitive insights of human empathy.

heading: 2 Related Work
text: Relevant prior works span two major areas: (1) social psychological theory on the relationship between prior experience and empathy and (2) datasets containing emotion or empathy ratings used for social-emotional reasoning tasks.

heading: 2.1 Empathy and Memory of Experiences
text: Empathy towards others is conditioned on situational (similarity between observer and target) and trait factors (personality, learning history) (Davis, 2004; Roshanaei et al., 2019). Furthermore, empathy is tied to important social functions such as prosocial behaviors, social connection, well-being, and psychiatric disorders (Morelli et al., 2015). A person’s past experiences and memories play an important role in both situational and trait empathy. This has been shown clearly in prior work on the social neuroscience of representations of self and other: an observer’s reaction to a target is elicited by language-based cognitive networks that trigger relevant memories with observer’s own feelings (Davis, 2004). Other studies use neuroimaging to show that prosocial behaviors may be due to synchronized representations of self and other (Decety
and Lamm). Memories of other people’s past experiences can modulate empathy, as these memories are used to simulate how one might feel in a new situation (Ciaramelli et al., 2013), and the vividness of memory of others’ experiences is tied to prosocial intentions (Gaesser, 2013).
Besides recalling prior experiences of oneself or others, the process of sharing personal experiences is strongly tied to empathy elicitation. Sharing personal memories makes conversations more truthful, engaging and communicates a person’s intentions or feelings (Pillemer, 1992; Bluck, 2003). The elicited empathy from experience sharing is even stronger when a listener responds with their own personal memories. In empathetic communication, both verbal (vividness of images, verb tense) and nonverbal (emotional gesturing, prosody) cues play a role in perceived empathy (Pillemer, 1992; Haase and Tepper).
Our dataset addresses all the previous points about empathetic communication: (1) self-reported annotations of situational and trait factors, (2) surveys of relevant social functions including social connection and wellbeing, and (3) video, audio, and transcripts of sessions with participants recalling their own memories and reflecting on others’ past experiences over time.

heading: 2.2 Social-Emotional Datasets
text: Beyond modeling empathy alone, more broadly, datasets for social and emotional benchmarking have garnered interest in recent years. Datasets such as MELD (Poria et al., 2019), M3ED (Zhao et al., 2022), and EmoInt-MD provide multimodal datasets annotated with emotion in conversations pooled from TV shows or movies. The Social-IQ dataset provides a multimodal benchmark for measuring social intelligence (Zadeh et al., 2019) and the related Social-IQA dataset benchmarks social intelligence with the text modality alone (Sap et al.,
2019). There are also datasets that capture the emotions of individuals during story sharing, such as SEND (Ong et al., 2021), emotions of dyads, such as IEMOCAP (Busso et al., 2008) and DAMI-P2C (Chen et al.), as well as datasets of naturalistic conversations, such as the CANDOR dataset (Reece et al., 2023).
Few prior works have provided multimodal datasets for empathy tasks alone, and most prior works in empathy benchmarking are text-only. Table 1 shows a summary of the most relevant datasets compared to our EMPATHICSTORIES++ dataset. One dataset, the OMG-Empathy dataset measures the emotional effect stories have on the listener (Barros et al., 2019), but contains a limited amount of data collected from in-lab settings. Two recent works present more substantial datasets: MEDIC, which contains video clips annotated with 3 labels to describe empathy between counselors and clients in psychotherapy sessions (Zhou’an_Zhu et al., 2023), and a motivational interviewing dataset for assessing therapist empathy (Tran et al., 2023). Prior works also provide datasets related to empathy focusing on single modalities. The EmpatheticDialogues dataset (Rashkin et al., 2019), the EDOS dataset (Welivita et al., 2021), the EmpathicStories dataset (Shen et al., 2023a), the Empathic Conversations dataset (Omitaomu et al., 2022), and Sharma et al. (2020) contain text-only benchmarks for empathetic conversations and stories. A few datasets focus on empathy and emotion in nonverbal contexts only, such as the EyeT4Empathy dataset (Lencastre et al., 2022) and iMiGUE dataset (Liu et al., 2021), which use gaze and gesture respectively.
In contrast to these prior works, our dataset is the first dataset that focuses on empathy in relation to past experiences, and is collected in-the-wild, over a long term deployment with longitudinal survey and interaction data, and contains self-annotated
empathy ratings.

heading: 3 Data Collection
text: We deployed 46 in-home robots, powered by ChatGPT,2 to converse with participants and record data. We recruited participants through mailing lists, and participants explicitly consented to data sharing. Our protocol was approved by our institution’s ethics review board. Five participants withdrew from data collection for reasons not related to the study protocol. Data collection took place over the course of a month, and participants were asked to complete between 6-12 conversation sessions with the robot (compensated $60 for 12 sessions). Figure 2 shows the robot station in the participants’ home and our data collection setup. The use of robots for data collection normalizes speaker-dependent characteristics that could add noise to the data from in-lab, human-human studies or acted scenarios (Wood et al., 2013b,a). While one might hypothesize that the use of a robot would users less expressive, prior work shows that embodied social agents still elicit empathy behaviors similar to that of human-human interaction (Spitale
2https://chat.openai.com/
et al., 2022; Wood et al., 2013b). We use the social robot to scaffold the interaction while still allowing for natural conversation. Within each session, participants were guided through a conversation with the agent using the following scheme.
1. Warm up phase. At the beginning of each section, the participant warms up to the robot through casual conversation about their day or the previous robot-participant interaction.
2. Story share phase. In this phase, the robot prompts the user to share a meaningful story from their journal or on their mind.
3. Story receive phase. The robot then addresses the user’s shared story by responding empathically, and retrieves 3 stories that the user might empathize with, using the empathic similarity retrieval model from Shen et al. (2023a).
4. Story reflection phase. We carefully designed reflection prompts based on narrative therapy approaches and emotion regulation (Gardner and Poole, 2009; White and Epston, 1990; Yoosefi Looyeh et al., 2014). Next, the
robot asks the participant to reflect on the following four areas: ways in which they related to the narrator, identifying the emotions of the narrator, regulating or comforting the narrator, and high-level takeaways from the story that the participant could apply to their own life.
5. Cool-down phase. Finally, the agent summarizes the session and thanks the participant.
Self-Report Survey Measures We collected selfreported measurements before the study, two weeks into the study, and at the one-month point. During our pre-study questionnaires, we administered the following trait surveys: the Big 5 Personality Test (Goldberg, 1993), the absorption scale dimensions of the Multidimensional Personality Questionnaire (measure ability to absorb into fictional experiences) (Cain et al., 2015), the Single Item Trait Empathy Scale (Konrath et al., 2018), and the following state surveys: the Compassionate Love for Humanity Scale (Sprecher and Fehr, 2005), and the UBC State Social Connection Scale (Lok and Dunn, 2022). Note that we use both the Compassionate Love for Humanity Scale and the UBC State Social Connection to measure overall “social connectedness.” For the mid-study and post-study questionnaires, all state surveys were repeated.
Interaction Data In the Story receive phase, participants read three personal stories retrieved based on the user’s own story. On the tablet, users rated their empathy toward each story using a slider on a scale of 1-5 (low to high).
Video and Audio Recordings During the study, each station completely recorded each interaction session, using the station’s built in Logitech 1080p webcam and MXL AC-44 USB Boundary microphone to obtain high-quality recordings of the participant’s face and voice. Note that we made clear when the system was recording the user in our study onboarding and through the robot’s ring light.
Transcripts Transcripts of all utterances by the robot and the participant were saved on a Firebase Realtime Database. The transcripts were obtained in real-time using the AssemblyAI streaming ASR.
Feature Extraction For each label, we trimmed the associated video clip to fit a context window of 120 frames (k = 120). This gives us 8 seconds worth of video context for videos that play at 15 frames per second. To augment the video clips, we’ve applied a sliding window technique every second. Consequently, this has yielded us a total of 99,357 clips for the Story share phase, and 84,705 samples for the Reflection phase.
• Vision.3 We use the normalized eye gaze direction, location of the head, location of 3D landmarks, and facial action units extracted from OpenFace (Baltrušaitis et al., 2016). We also extract frame-wise image features from the penultimate layer of ResNet50 (He et al., 2015). The two feature vectors (obtained from OpenFace and ResNet50) are concatenated per timestep to be used as the final visual input (dimension/timestep is F = 2762).
• Audio. We use openSMILE (Eyben et al., 2010) to extract low level acoustic features (i.e. loudness, alpha ratio, etc., F = 65)
• Language. We convert video transcripts and story contents into text embeddings via pretrained Glove (glove.840B.300d) (Pennington et al., 2014) word embedding and Sentence BERT (Reimers and Gurevych, 2019) (F = 300, F = 384 respectively).

heading: 4 Dataset Statistics and Properties
text: The EMPATHICSTORIES++ dataset comprises video, audio, and text data from 269 sessions collected from 41 distinct participants, along with selfreported survey and interaction data. Each video is a .avi file recorded at 15fps, whose cumulative length is 3,180 minutes (53 hours). The total number of utterances is 53,80, or about 20 per session (fixed for each interaction phase), totalling 337,147 words (1,258 per session).
Figure 3a shows the distribution of video lengths across sessions, ranging from 2 to 29 minutes (mean = 12 min, s.d. = 4.5 min). Figure 3b depicts a similar distribution for spoken word counts. These ranged between 40 and 3418 words (mean = 1258 words, s.d. = 531 words). Participants felt varying levels of empathy towards with the stories they received, as the distribution (Figure 3c) of their empathy ratings on 1-5 scale shows (mean = 3.3, s.d. = 1.2).
Figure 4 depicts the demographic information of the participants. Figures 4a-4d show the distributions of age, gender, trait empathy, and trait absorption. Measurements of the Big 5 personality traits are shown in the radar chart Figure 4e.
3As illustrated in Figure 1, we additionally provide the whole-body (bodies, hands and faces) 2D/3D poses obtained from DOPE (Weinzaepfel, Philippe and Brégier, Romain and Combaluzier, Hadrien and Leroy, Vincent and Rogez, Grégory) in our dataset.
The change in levels of Social Connection, Compassionate Love for Humanity, and Wellness (see Figure 3) across the month-long study are shown in Figure 4f. Participants shared vulnerable and meaningful stories across diverse topics (Appendix A).
Our dataset is notable in that it (1) is captured in-the-wild, in participants’ homes (2) contains longitudinal data, with trait and state surveys, and (3) is self-annotated, which is crucial for a subjective psychological process like empathy.

heading: 5 Experiments

heading: 5.1 Task Definition
text: We formulate the multimodal empathy prediction task as follows: At time t, where t is the timestep in which we want to predict each participant’s empathy levels for the story, we are given the [t− k/2, ..., t+ k/2] interval of contextual video information (during the Story Share and Reflection phases), where k is the number of context frames.
For each clip, we extract features from three modalities: text, audio, and video. Each modality has distinct temporal and feature dimension, denoted as T{V,A,T} × F{V,A,T}. The corresponding contextual behavior features for each modality can be viewed as XT ∈ RTT×FT , XA ∈ RTA×FA , and XV ∈ RTV ×FV , respectively. The comprehensive multimodal feature set is represented as X = [XT , XA, XV ]. Finally, we train a model fθ(·) that takes X as input and outputs a multimodal representation Z = fθ(X), which is further used to calculate empathic similarity score sim(Z,E(Si)) where sim(·) is a similarity metric (e.g., cosine similarity), and E(Si) is the embedding of the ith story Si (i = 1, 2, 3). Finally, this similarity score is compared with the empathic label y to calculate the loss.

heading: 5.2 Models
text: Attention-based multimodal Emotion Reasoning model (AMER) (Shen et al., 2020): AMER is a model designed to facilitate the task of multimodal emotion reasoning in videos. It employs an attention-based approach to model intra- and interpersonal emotion contexts, propagation, and prior knowledge of personalities.
Tensor Fusion Network (TFN) (Zadeh et al., 2017): TFN is a representative tensor-based network, initially developed for multimodal sentiment
analysis. It carries out an outer tensor-product operation on the embeddings of modalities to create a unified multimodal space.
Late-Fusion LSTM (LF-LSTM) (Hochreiter and Schmidhuber, 1997): LF-LSTM is a model that separately constructs LSTMs for linguistic, visual, and acoustic inputs. It fuses the final hidden states of these three LSTMs, creating a comprehensive sentence-level multimodal representation.
Early-Fusion LSTM (EF-LSTM) (Hochreiter and Schmidhuber, 1997): EF-LSTM assembles linguistic, visual, and acoustic features at each time step, utilizing an LSTM to construct a sentencelevel multimodal representation.
EmpathicStoriesBART (Shen et al., 2023b): EmpathicStoriesBART is a distinctive model finetuned to compute empathic similarity in personal narratives using three key story features. Validated in a user study, it outperforms traditional semantic similarity models, highlighting its potential for our task.
GPT-4 (OpenAI, 2023): GPT-4, a state-of-theart closed-source language model capable of deep contextual understanding and producing highly relevant responses. GPT models have been evaluated for empathetic response generation (Lee et al.).
Implementation details and prompts are included in Appendix B and C.

heading: 6 Results and Discussion

heading: 6.1 Automatic Evaluation
text: To evaluate the quality of empathy predictions, we follow previous work (Shen et al., 2023a) and report Pearson’s correlation, Spearman’s correlation, accuracy, F1-scores and the mean squared error. For correlations, we calculate the cosine similarity between the multimodal representation and the embedding of the stories and compare these similarity scores with the human-rated empathy labels. For interpretability, we split the scores into binary similar/dissimilar categories and compute the accuracy and F1 scores.
Table 2 shows the performance of state-of-theart multimodal (video, audio and text) models when given the user’s Story Share context (video and audio) + the story they read (text) as inputs, and their empathy ratings as labels. In the context of Story Share, GPT-4 showed the highest Pearson’s correlation (r = 0.232) and Spearman’s correlation (ρ = 0.176) with t-only input. Notably, it also recorded the highest accuracy (Acc = 0.825) and F1-score (F1 = 0.506) which aligns well with the observation that participants in the Story Share setting were more focused on conveying their story, rather than on expressive verbal and non-verbal behaviors. Conversly, the performance of models in the context of user Reflection (reflections on a read story) is outlined in Table 3. Here, LF-LSTM demonstrated the highest Pearson’s correlation (r = 0.560) and Spearman’s correlation (ρ = 0.559) with v+t inputs. While GPT-4 continued to show the highest accuracy and F1-scores, it’s worth noting that among multimodal models, AMER showed comparable
performance (Acc = 0.688, F1 = 0.665) even with a significantly smaller number of parameters and using only audio with text inputs.

heading: 6.2 Ablation Studies
text: Here, we analyze the influence of various input modalities on six models in both Story Share and Reflection settings, focusing particularly on the impact of text-only inputs.
In the Story Share scenario, across different models and input modalities, no significant performance improvements were observed as we add more input modalities to text. Interestingly, using t-only input showed the best performance in Acc across all multimodal models. In contrast, in the Reflection scenario, where both verbal and non-verbal expressions plays a vital role, AMER showed remarkable performance improvements (26.90% in Acc) when adding a to t and 14.02% for EF-LSTM when using v+a inputs. Also, by adding v to t, all multimodal model showed performance improvements (10.36% for Acc and 26.28% for F1 in average). However, EmpathicStoriesBART and GPT-4 model, which solely use t-only input, outperforms all other models, achieving an impressive accuracy of 0.737. This significant performance, combined with a high F1 score of 0.762 and 0.750, underscores the potential of task and context specificity and the use of key story features to identify moments of empathy. To confirm the robustness of these results, we applied majority voting based on the label distribution, which resulted in an accuracy of 0.750 and an F1 score of 0.839.

heading: 7 Conclusion
text: This paper presents EMPATHICSTORIES++, the first in-the-wild, long-term, multimodal dataset on empathy towards personal experiences, which can be used to quantitatively evaluate empathy as it relates to one’s past experiences. Our dataset is self-annotated with empathy ratings and psychometric surveys. We present and benchmark a task on predicting user empathy from their interaction contexts. We observe that modality selection impacts model performance and is context-dependent. In the Story Share phase, where textual context was dominant, GPT-4 with text input performed the best in most metrics. However, in the Reflection phase, where introspective verbal and nonverbal expressions are abundant, using v+t inputs showed 26.28% improvement in average for F1
score, demonstrating their proficiency in extracting meaningful insights from multi-modal inputs. Our work provides a valuable resource for future work in empathetic AI, quantitative exploration of cognitive insights, and empathy modeling. We publicly release our dataset to foster advancements in social-emotional AI.

heading: Acknowledgments
text: We would like to thank the participants of our work for contributing to this dataset. We would also like to thank Jon Ferguson and Audrey Lee for their technical contributions in deploying the robot stations for data collection. This work was supported by an NSF GRFP under Grant No. 2141064
Ethics Statement
Our dataset contains intimate, personal stories and video-audio data of participants necessary for modeling empathetic response. However, this type of naturalistic data is sensitive and private. As such, we made sure participants explicitly consented with data sharing, and our protocol was approved by our institutions ethics review board. Furthermore, in the design of our robot station, we made sure it was clear whenever the robot was listening (through a blue ring light) and that data would only be recorded during sessions, not the entire duration the robot was in a participant’s home. We made sure to store videos on a private, lab-hosted server. For transparency we note that 17% of participants mentioned concerns of the robot infringing their privacy/security during our post-study interviews. While our dataset contains intimate information, we believe that such a resource is necessary in advancing science about empathy, which by nature occurs in personal and natural settings. We will ensure that distribution of the dataset is only granted upon ethics review board approval, and that the dataset is only used towards the goal of furthering positive empathy research in the future.
Limitations and Future Work
The main limitation of our work is the limited sample size afforded by use of a physical robot. However, we believe the use of an embodied agent is essential for our data collection, as embodied agents provide experiences closer to that of natural human interaction than virtual interactions. Future work can use our system to replicate the data collection through a physically embodied robot.
Another limitation of our experimental results is that we only ablated contribution of modalities, but did not further interpret behavioral cues that might influence model performance. As such, these results are less interpretable due to lack of additional fine-grained annotations. Future work can obtain fine-grained annotations of the video data for empathy-relevant behavioral cues such as arousal, valence, self disclosure, etc.
Our dataset is a valuable resource for furthering research in empathy modeling for AI systems. Novel future directions to explore could include personalized modeling of empathy patterns, using the longitudinal data as well as understanding cognitive insights behind when empathy arises in personal story sharing.

heading: A Story Topics
text: The topics in Figure 5 were obtained as follows: ada-v002 embeddings of stories were calculated via the OpenAI API, and a UMAP model was fit on the data to reduce the 1536 dimension vectors to x and y coordinates using cosine similarity as the distance measure and clusters were obtained with K-means.
B Implementation Details
We train our models on 4 NVIDIA RTX A6000 with a batch size of 64 for 10 epochs. We use the AdamW (Loshchilov and Hutter, 2019) optimizer
with an initial learning rate of 1e-4 with a scheduler StepLR that decays the learning rate by 0.1 (γ) every 5 epochs (step_size). For the loss function, we use the Mean Squared Error (MSE). For the dataloader, we first conduct oversampling based on the empathy ratings due to its imbalance distribution as shown in Figure 3c. Next, we separate participants into train/valid/test sets in the ratio of 0.7/0.2/0.1 to ensure the model does not see the participants who were in the train sets. All models except for GPT-4 and EmpatheticStoriesBART were re-implemented to output multimodal representations that can be used to calculate similarities of story embeddings. We follow the default model parameters from the original implementations.

heading: C Prompting
text: We include prompts for GPT-4 benchmarking below: Story Sharing:
• System prompt: You are a psychologist with expertise in analyzing empathy. You can predict how much people might empathize with each other, based on their past experiences.
• User prompt: You will receive two stories, one from person A and the other from person B. Please predict, on a scale from 0 to 1, how
much person A would empathize with B’s story. Return just the number, no other text.
Reflection:
• System prompt: You are a psychologist with expertise in analyzing empathy. You can predict how much people might empathize with each other, based on their past experiences.
• User prompt: You will receive a story and conversation between person A and person B about person A’s reflections about the story. Based on this, please predict, on a scale from 0 to 1, how much person A would empathize with the story. Return just the number, no other text.

heading: Future Work