Papers by Guangxuan Xu
EnDex: Evaluation of Dialogue Engagingness at Scale (2022.findings-emnlp)
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| Challenge: | Existing models that measure engagement use expensive human annotas and abstract definitions of the term. |
| Approach: | They propose a human-reaction based model to evaluate dialogue engagingness . they propose combining distant-supervision with a theoretical foundation for engagement . |
| Outcome: | The proposed model is trained on 80k Reddit-based engagement datasets . it uses distant-supervision from human-reaction feedback to evaluate dialogue engagementness . |
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)
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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang
| Challenge: | Dialogue safety problems severely limit the real-world deployment of generative conversational models. |
| Approach: | They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings. |
| Outcome: | The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples. |
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data augmentation produce low readability or semantic consistency. |
| Approach: | They propose a framework which augments data through reinforcement learning guided conditional generation. |
| Outcome: | The proposed framework improves F1 performance on three different classification tasks by 8.7% on average when given only 10% of the whole data for training. |
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales (2023.acl-long)
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| Challenge: | Social biases and stereotypes are embedded in our culture through their presence in our stories. |
| Approach: | They propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. |
| Outcome: | The proposed framework extracts a story’s verb-based event chain for each of its characters as well as character attributes such as gender. |
A Grounded Preference Model for LLM Alignment (2024.findings-acl)
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Tahira Naseem, Guangxuan Xu, Sarathkrishna Swaminathan, Asaf Yehudai, Subhajit Chaudhury, Radu Florian, Ramón Astudillo, Asim Munawar
| Challenge: | Large Language Models (LLMs) suffer from factual inconsistency and hallucination despite recent advances . training a preference model requires substantial human annotation, which is expensive and labor-intensive. |
| Approach: | They propose to generate synthetic grounded preference data and train a Grounded Preference Model to assess the overall quality of grounded responses. |
| Outcome: | The proposed model can generate much better grounded responses as judged by GPT4 and achieves the TRUE faithfulness Benchmark. |