Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
Computational modeling of semantic change (2024.eacl-tutorials)
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| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
| Outcome: | In historical linguistics, tools and methods have been developed to analyse the process . they include categorisations of types of change, causes and mechanisms . but traditional methods, while informative, are often based on small, carefully curated samples. |
Item Response Theory for Natural Language Processing (2024.eacl-tutorials)
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| Challenge: | This tutorial introduces the wider NLP community to Item Response Theory (IRT) existing software for fitting IRT models is limited by human-data sized constraints. |
| Approach: | They will introduce IRT and the mathematical foundations which make IRT models. |
| Outcome: | This tutorial aims to introduce the wider NLP community to Item Response Theory and show its benefits for a number of NLP tasks. |
Language + Molecules (2024.eacl-tutorials)
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| Challenge: | In the last year, instruction-following language models have surged in popularity. |
| Approach: | This tutorial will provide an introduction to applying natural language-driven solutions to chemistry problems. |
| Outcome: | This tutorial will provide an introduction to this area of research. it requires no knowledge outside mainstream NLP, and it will enable participants to begin exploring relevant research. |
Transformer-specific Interpretability (2024.eacl-tutorials)
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| Challenge: | Transformers are dominant play-ers in various scientific fields, but their inner workings remain opaque. |
| Approach: | This tutorial presents a trending approach to interpreting Transformers . it uses specific features of the Transformer architecture to quantify context- mixing interactions . |
| Outcome: | This tutorial aims to show how a new trending approach can be applied to Transformer-based models. |
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
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| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
| Outcome: | The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects. |