| Date | Presenter | Slides | Title |
|---|---|---|---|
| 09/16 | Saptarshi Ghosh | Metonymy in NLP | |
| Abstract: During the presentation, I will discuss:
1. What does metonymy mean, types of metonymies, some of the ongoing work in this field.
2. Inferencing abilities of LLM's in detecting metonymy and some of the models we developed.
3. Our current research using LLM's to develop a high-quality metonymic dataset and the results of testing it out on our models
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| 09/23 | Abhigyan Acherjee | Paper Reading | |
|
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (ACL 2023)
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| 09/30 | Phan Anh Duong & Cat Luong | Paper Reading | |
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"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling (NAACL 2024)
Also check: Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation (ACL 2017) |
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| 10/07 | Mohammad Saim | Paper Reading | |
| Measuring Political Bias in Large Language Models: What Is Said and How It Is Said (ACL 2024) | |||
| 10/14 | Linfeng Liu | Paper Reading | |
| LoRA: Low-Rank Adaptation of Large Language Models | |||
| 10/21 | Rudrashis Poddar | Ongoing Research on frame semantic parsing. | |
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| 10/28 | Multi | Paper Reading | |
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| 11/04 | Dylan Hutson | ||
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| 11/18 | Phan Anh Duong & Cat Luong | ||
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| 11/25 | Nick Jarvis | ||
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