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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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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. 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. Published in SemEval (Proceedings of the 19th International Workshop on Semantic Evaluation), 2025 Task description paper for SemEval-2025 Task 1, which introduces the first multimodal idiomaticity task, advancing the representation of idiomatic expressions across text and image modalities. Recommended citation: Thomas M. R. Pickard, Aline Villavicencio, Maggie Mi, Wanqiu He, Dylan Phelps, and Marco Idiart. 2025. SemEval-2025 Task 1: AdMIRe — Advancing Multimodal Idiomaticity Representation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025). Association for Computational Linguistics. Published in ACL Main (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics), 2025 This paper investigates how large language models handle idiomatic language in context, revealing systematic failures in contextual understanding of idiomatic expressions. Recommended citation: Maggie Mi, Aline Villavicencio, and Nafise Sadat Moosavi. 2025. Rolling the Dice on Idiomaticity: How LLMs Fail to Grasp Context. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). Association for Computational Linguistics. Published in EMNLP Main (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing), 2025 This paper proposes a framework for anticipating model blind spots by modeling the relationship between input perception and prediction errors, enabling proactive identification of failure cases. Recommended citation: Maggie Mi, Aline Villavicencio, and Nafise Sadat Moosavi. 2025. From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025). Association for Computational Linguistics. Published in LREC 2026 (Proceedings of the 2026 Joint International Conference on Computational Linguistics, Language Resources and Evaluation), 2026 This paper investigates how explicit reasoning chains affect the ability of language models to detect idiomatic expressions, providing analysis of reasoning-augmented approaches to idiomaticity detection. Recommended citation: Dylan Phelps, Rodrigo Wilkens, Edward Gow-Smith, Thomas M. R. Pickard, Maggie Mi, and Aline Villavicencio. 2026. Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection. In Proceedings of the 2026 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2026). Published in Accepted to ACL 2026 (Main), arXiv preprint available., 2026 This paper explores how incorporating language learning tasks into the pre-training process can improve the linguistic competence of language models. Recommended citation: Atsuki Yamaguchi, Maggie Mi, and Nikolaos Aletras. 2026. Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks. arXiv preprint. Published: This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown! Published: This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. Undergraduate course, University 1, Department, 2014 This is a description of a teaching experience. You can use markdown like any other post. Workshop, University 1, Department, 2015 This is a description of a teaching experience. You can use markdown like any other post.
Download Paper</p> </article> </div> ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification
Download Paper</p> </article> </div> SemEval-2025 Task 1: AdMIRe — Advancing Multimodal Idiomaticity Representation
Download Paper Rolling the Dice on Idiomaticity: How LLMs Fail to Grasp Context
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors
Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection
Download Paper Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
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