Papers by Sahand Sabour
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond (2023.emnlp-main)
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| Challenge: | Existing methods to generate text in mental health are limiting, but they are effective for many tasks. |
| Approach: | They propose a task-adaptive tokenizer that allows for the integration of task-specific tokens into the pre-trained model's tokenization step. |
| Outcome: | The proposed tokenization approach improves generation performance on psychological question-answering tasks in Chinese and English while using 60% fewer tokens. |
PAL: Persona-Augmented Emotional Support Conversation Generation (2023.findings-acl)
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| Challenge: | Recent work has demonstrated the effectiveness of dialogue models in providing emotional support due to the lack of human resources for mental health support. |
| Approach: | They propose a framework for dynamically inferring and modeling seekers’ persona from the conversation history and a model that leverages persona information to provide personalized emotional support. |
| Outcome: | The proposed model outperforms baseline models on the studied benchmark. |
Towards Emotional Support Dialog Systems (2021.acl-long)
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| Challenge: | Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. |
| Approach: | They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems. |
| Outcome: | The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models. |
Rethinking and Refining the Distinct Metric (2022.acl-short)
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| Challenge: | Existing methods for calculating distinct scores have evident biases that assign higher penalties to longer sequences. |
| Approach: | They propose to scale the number of distinct tokens based on their expectations. |
| Outcome: | The proposed metric removes evident biases in the original distinct score . the proposed meter correlates better with human judgment in evaluating response diversity . |
EmoBench: Evaluating the Emotional Intelligence of Large Language Models (2024.acl-long)
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Sahand Sabour, Siyang Liu, Zheyuan Zhang, June Liu, Jinfeng Zhou, Alvionna Sunaryo, Tatia Lee, Rada Mihalcea, Minlie Huang
| Challenge: | Existing benchmarks for Emotional Intelligence (EI) focus on emotion recognition, neglecting essential EI capabilities. |
| Approach: | They propose a benchmark that proposes a comprehensive definition for machine EI . they propose 400 hand-crafted questions in English and Chinese to evaluate EI. |
| Outcome: | The proposed benchmarks focus on emotion recognition, neglecting EI capabilities . they are constructed from existing datasets, which include frequent patterns and errors . the proposed benchmark includes questions in English and Chinese that require thorough reasoning and understanding . |
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation (2023.findings-acl)
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| Challenge: | Crowdsourced dialogue corpora are limited in scale and topic coverage due to the expensive cost of data curation. |
| Approach: | They construct an augmented dataset for the emotional support conversation task using large language models for dialogue augmentation. |
| Outcome: | The proposed approach outperforms baselines of dialogue augmentation and improves the model's generalization ability to open-domain topics. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |