Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
| Approach: | This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks . |
| Outcome: | This tutorial aims to examine the evolution of general-purpose large language models (LLMs) the authors argue that the evolution is dependent on the availability of training data and the scale of the models. |
Countering Hateful and Offensive Speech Online - Open Challenges (2024.emnlp-tutorials)
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Leon Derczynski, Marco Guerini, Debora Nozza, Flor Miriam Plaza-del-Arco, Jeffrey Sorensen, Marcos Zampieri
| Challenge: | a comprehensive understanding of the field is needed to maintain respectful and inclusive online environments. |
| Approach: | This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. |
| Outcome: | This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. |
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
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| Challenge: | Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments. |
| Approach: | They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics. |
| Outcome: | The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas. |
Introductory Tutorial: Reasoning with Natural Language Explanations (2024.emnlp-tutorials)
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| Challenge: | Existing paradigms for explanation-based NLIs lack a clear understanding of the nature of human reasoning. |
| Approach: | They propose to use natural language explanations to build models that address downstream tasks through explicit construction of a natural language. |
| Outcome: | In contrast to the existing paradigm based on Deep Learning, explanation-based NLI focuses on developing and evaluating models that address downstream tasks through the explicit construction of a natural language explanation. |
AI for Science in the Era of Large Language Models (2024.emnlp-tutorials)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions. |
| Approach: | This tutorial explores the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals. |
| Outcome: | This tutorial will explore the application of large language models to three crucial categories of scientific data. |
Human-Centered Evaluation of Language Technologies (2024.emnlp-tutorials)
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| Challenge: | a lack of human-centered considerations about people’s needs for language technologies is causing an “evaluation crisis” in NLP. |
| Approach: | This tutorial introduces perspectives and methodologies from human-computer interaction (HCI) it will introduce what to evaluate for, how generalizable the results are to the real-world contexts, and pragmatic costs to conduct the evaluation. |
| Outcome: | This tutorial introduces perspectives and methodologies from human-computer interaction (HCI) the tutorial will also encourage reflection on how these HCI perspectives and methods can complement NLP evaluation through Q&A discussions and a hands-on exercise. |