Papers by Sarah Gupta
An Audit on the Perspectives and Challenges of Hallucinations in NLP (2024.emnlp-main)
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Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, Shomir Wilson
| Challenge: | 103 peer-reviewed publications on hallucination in large language models (LLMs) are characterized by a lack of agreement with the term ‘hallucination’ in the field of NLP. |
| Approach: | They examine 103 peer-reviewed publications on hallucination in large language models (LLMs) and conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on halllucination. |
| Outcome: | The findings highlight the need for explicit definitions and frameworks outlining hallucination within NLP and highlight potential challenges. |
Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks (N19-3)
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| Challenge: | Automated extraction methods for vowels are available, but coding rhoticity has lagged behind. |
| Approach: | They use Neural Networks/Deep Learning to train a model on 208 speakers in Boston . they find that there is no reliable method for classifying r-dropping . |
| Outcome: | The proposed method trains a model on 208 speakers in Boston, Massachusetts. |
Editing Common Sense in Transformers (2023.emnlp-main)
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Anshita Gupta, Debanjan Mondal, Akshay Sheshadri, Wenlong Zhao, Xiang Li, Sarah Wiegreffe, Niket Tandon
| Challenge: | Currently, the performance of transformer-based model editing methods is limited to statements about encyclopedic knowledge with a single correct answer. |
| Approach: | They propose to improve MEMIT's model editing algorithm by varying edit tokens and improving the layer selection strategy to improve commonsense knowledge. |
| Outcome: | The MEMIT editing algorithm outperforms baseline models on PEP3k and 20Q datasets while fine-tuning baselines shows significant trade-offs. |