Maggie Mi

Maggie Mi

PhD Student in NLP/Computational Linguistics

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Mmi01 at The BabyLM Challenge: Linguistically Motivated Curriculum Learning for Pretraining in Low-Resource Settings

Published in CoNLL (Conference on Computational Natural Language Learning), 2023

This paper presents our findings for the BabyLM Challenge (Warstadt et al., 2023). Our exploration is inspired by vanilla curriculum learning (Bengio et al., 2009) and we explored the effect of linguistic complexity in forming the best curriculum for pre-training. In particular, we explore curriculum formations based on dependency-based measures (dependents per token, average dependency distance) and lexical-based measures (rarity, density, dispersion and diversity). We found that, overall, models pretrained using curriculum learning were able to beat the performance of a noncurriculum learning pre-trained model. Furthermore, we notice using different linguistic metric for measuring complexity lead to advantageous performance for some tasks, but not all. We share our results and analysis in the hope that it can provide beneficial insights for future work

Recommended citation: Maggie Mi. 2023. Mmi01 at The BabyLM Challenge: Linguistically Motivated Curriculum Learning for Pretraining in Low-Resource Settings. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 269–278, Singapore. Association for Computational Linguistics.
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Paper Title Number 4

Published in GitHub Journal of Bugs, 2024

This paper is about fixing template issue #693.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection

Published in MWE | UDW | WS, 2024

Despite the recent ubiquity of large language models and their high zero-shot prompted performance across a wide range of tasks, it is still not known how well they perform on tasks which require processing of potentially idiomatic language. In particular, how well do such models perform in comparison to encoder-only models fine-tuned specifically for idiomaticity tasks? In this work, we attempt to answer this question by looking at the performance of a range of LLMs (both local and software-as-a-service models) on three idiomaticity datasets: SemEval 2022 Task 2a, FLUTE, and MAGPIE. Overall, we find that whilst these models do give competitive performance, they do not match the results of fine-tuned task-specific models, even at the largest scales (e.g. for GPT-4). Nevertheless, we do see consistent performance improvements across model scale. Additionally, we investigate prompting approaches to improve performance, and discuss the practicalities of using LLMs for these tasks.

Recommended citation: Dylan Phelps, Thomas M. R. Pickard, Maggie Mi, Edward Gow-Smith, and Aline Villavicencio. 2024. Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection. In Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024<\em>, pages 178–187, Torino, Italia. ELRA and ICCL.
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ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification

Published in SemEval (Lexical and Computational Semantics and Semantic Evaluation), 2024

This paper presents our findings for SemEval2024 Task 4. We submit only to subtask 1, applying the text-to-text framework using a FLAN-T5 model with a combination of parameter efficient fine-tuning methods - low-rankadaptation and prompt tuning. Overall, we find that the system performs well in English, but performance is limited in Bulgarian, North Macedonian and Arabic. Our analysis raises interesting questions about the effects of labelorder and label names when applying the text-to-text framework.

Recommended citation: Meredith Gibbons, Maggie Mi, Xingyi Song, and Aline Villavicencio. 2024. ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)<\em>, pages 1860–1867, Mexico City, Mexico. Association for Computational Linguistics.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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