EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction (2021.eacl-main)
Copied to clipboard
| Challenge: | EmpathBERT is a demographic-aware framework for empathy prediction based on BERT. |
| Approach: | They propose a demographic-aware framework for empathy prediction based on BERT and utilize user demographics to analyze user responses to stimulative news articles. |
| Outcome: | The proposed framework surpasses machine learning and deep learning models and highlights the importance of demographic information in the responses. |
Similar Papers
EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance Metric (2026.findings-acl)
Copied to clipboard
| Challenge: | Empathy evaluation metrics are lacking in the competitions, and classical dialogue evaluation metrics require further investigation. |
| Approach: | They propose a framework which combines fine-tuned models, large language models, classical dialogue evaluation metrics, and a novel metric. |
| Outcome: | The proposed framework improves on the WASSA 2024 benchmark and shows a statistically significant 8% improvement on the EX dataset. |
Towards Persona-Based Empathetic Conversational Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. |
| Approach: | They propose a task towards persona-based empathetic conversations and propose e-learning model CoBERT that can be used to train persona on emmpathetic conversations. |
| Outcome: | The proposed model improves empathetic responding more when trained on e-mpathetic conversations than non-empathy ones. |
Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique (2022.aacl-short)
Copied to clipboard
| Challenge: | In this paper, we analyze the variations in gender and racial biases in BERT-like language models when exposed to different demographic groups. |
| Approach: | They analyze gender and racial biases in BERT-like language models when exposed to different demographic groups. |
| Outcome: | The proposed model can mitigate biases in text authored by disadvantaged demographic groups compared to advantaged groups . the proposed model is agnostic to the language of the speakers behind the language . |
A Large-Scale Dataset for Empathetic Response Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing empathetic datasets are limited in size and cost due to the cost of manual labor. |
| Approach: | They propose to annotate 1M dialogues with 32 fine-grained emotions and eight empathetic response intents and the Neutral category using a silver dataset. |
| Outcome: | The proposed pipeline compares the quality of the proposed dataset with a state-of-the-art gold dataset using offline experiments and visual validation methods. |
Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model’s Empathy (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models’ ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. |
| Approach: | They propose a framework to investigate how LLMs’ cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. |
| Outcome: | The proposed framework examines 315 unique personas from age, culture, and gender across four LLMs. |
EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies lack the perception of fine-grained dialogue emotion propagation, and have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response. |
| Approach: | They propose to use commonsense reasoning and reinforcement learning to generate empathetic response based on in-context commonsensing and contextual reasoning to broaden cognitive boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art models in automatic and human evaluation. |
From Traits to Empathy: Personality-Aware Multimodal Empathetic Response Generation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches focus on acquiring affective and cognitive knowledge from text, but neglect the unique personality traits of individuals and the inherently multimodal nature of human face-to-face conversation. |
| Approach: | They propose a multimodal dialogue system that generates empathetic responses from a perspective that considers the personality traits of users. |
| Outcome: | The proposed system generates empathetic responses from a multimodal perspective and analyzes multimodal data to understand the user’s emotional state and situation. |
EmpHi: Generating Empathetic Responses with Human-like Intents (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing empathetic dialogue models lack emotion-dependent response generation . elaine mccartney: "i'm sorry to hear that! " |
| Approach: | They propose a model to generate empathetic responses with human-consistent intents . they aim to address the bias of the empathic intent distribution between epd models and humans . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of empathy, relevance, and diversity on automatic and human evaluation. |
Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses (2025.findings-emnlp)
Copied to clipboard
| Challenge: | large language models (LLMs) increasingly assist subjective decision-making . prior work uses aggregate human judgments, but demographic variation and its linguistic drivers remain underexplored. |
| Approach: | They analyze how demographic background and empathy level correlate with LLM-generated dilemma responses . they also identify markers that predict group-level differences . |
| Outcome: | The authors show that demographic background and empathy level correlate with LLM preferences . their findings highlight the need for demographically informed LLM evaluations. |
ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction? (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for moral foundation prediction are limited due to lack of annotated data. |
| Approach: | They propose a framework for fine-tuning a pre-trained language model to the task of moral foundation prediction. |
| Outcome: | The proposed framework outperforms state-of-the-art methods for moral foundation prediction with an average increase of 35% in the out-of domain scenario. |