We describe an approach for aligning an LLMbased dialogue agent for long-term social dialogue, where there is only a single global score given by the user at the end of the session.In this paper, we propose the usage of denser naturally-occurring multimodal communicative signals as local implicit feedback to improve the turn-level utterance generation.Therefore, our approach (dubbed GELI) learns a local, turn-level reward model by decomposing the human-provided Global Explicit (GE) sessionlevel reward, using Local Implicit (LI) multimodal reward signals to crossmodally shape the reward decomposition step.This decomposed reward model is then used as part of the RLHF pipeline to improve an LLM-based dialog agent.We run quantitative and qualitative human studies on two large-scale datasets to evaluate the performance of our GELI approach, and find that it shows consistent improvements across various conversational metrics compared to baseline methods.

Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories.While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, through narrative style.Yet the relationship between empathy and narrative style is not fully understood.In this work, we empirically examine and quantify this relationship between style and empathy using LLMs and large-scale crowdsourcing studies.We introduce a novel, theory-based taxonomy, HEART (Human Empathy and Narrative Taxonomy) that delineates elements of narrative style that can lead to empathy with the narrator of a story.We establish the performance of LLMs in extracting narrative elements from HEART, showing that prompting with our taxonomy leads to reasonable, human-level annotations beyond what prior lexicon-based methods can do.To show empirical use of our taxonomy, we collect a dataset of empathy judgments of stories via a large-scale crowdsourcing study with N = 2, 624 participants.1 We show that narrative elements extracted via LLMs, in particular, vividness of emotions and plot volume, can elucidate the pathways by which narrative style cultivates empathy towards personal stories.Our work suggests that such models can be used for narrative analyses that lead to human-centered social and behavioral insights.1. Flatness/roundness (Keen, 2006) of the charac-

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 EM-PATHICSTORIES, 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 finetune 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.