Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries
From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024.lrec-tutorials)
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| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
| Approach: | This tutorial will review cutting-edge research in MLLMs and examine the impact of ML in learning and reasoning. |
| Outcome: | This course will review cutting-edge research in MLLMs and examine the impact of ML models on learning, learning, and multimodal reasoning. |
Geo-Cultural Representation and Inclusion in Language Technologies (2024.lrec-tutorials)
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| Challenge: | audi et al.: training and evaluation of language models rely on semi-structured data that is annotated by humans . e-learning tools do not integrate rich and diverse community perspectives into language technologies . |
| Approach: | They will examine how different socio-cultural perspectives influence what is taken as ground truth by models. |
| Outcome: | This tutorial examines how different socio-cultural perspectives influence representations of global concepts. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)
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| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
| Approach: | This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods. |
| Outcome: | The tutorial assumes little familiarity with metrics, datasets, prompts and benchmarks . it will compare traditional methods to newly developed methods . |
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2024.lrec-tutorials)
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| Challenge: | a tutorial on computational argumentation is updated to address the problem of argument quality . argument quality is a field of interdisciplinary research that connects natural language processing to social sciences . |
| Approach: | They present an updated version of the EACL 2023 tutorial on argument quality . they will focus on the notions of argument quality across disciplines . |
| Outcome: | The updated version of the EACL 2023 tutorial focuses on argument quality assessment . the authors will focus on the interface between Argument Mining and Deliberation Theory . |
Knowledge Editing for Large Language Models (2024.lrec-tutorials)
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| Challenge: | Large Language Models (LLMs) are not immune to issues of factual accuracy or logically consistent. |
| Approach: | This tutorial will present cutting-edge methods and practical tools for editing Large Language Models (LLMs). |
| Outcome: | The aim of this course is to familiarize researchers with the latest advancements and emerging strategies in the realm of knowledge editing for LLMs. |
The DBpedia Databus Tutorial: Increase the Visibility and Usability of Your Data (2024.lrec-tutorials)
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| Challenge: | Linked Open Data tutorial introduces DBpedia Databus, a FAIR data publishing platform . aimed at addressing data production and consumption challenges faced by knowledge graph stakeholders . |
| Approach: | This tutorial introduces DBpedia Databus, a FAIR data publishing platform . it addresses challenges faced by data producers and consumers . |
| Outcome: | This tutorial addresses challenges faced by data producers and consumers using DBpedia Databus. |
NLP for Chemistry – Introduction and Recent Advances (2024.lrec-tutorials)
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| Challenge: | This tutorial will provide an introductory overview to a relatively underrepresented application domain: chemistry. |
| Approach: | This tutorial will provide an introductory overview to a number of recent applications of natural language processing to chemistry. |
| Outcome: | This tutorial will provide an overview of the latest applications of natural language processing to chemistry. |
Formal Semantic Controls over Language Models (2024.lrec-tutorials)
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| Challenge: | Text embeddings provide a concise representation of the semantics of sentences and larger spans of text, rather than individual words, capturing a wide range of linguistic features. |
| Approach: | They propose to shorten the gap between latent semantics and formal symbolics by comparing distributional models to symbolic models grounded on formal linguistics and well-defined mathematical properties. |
| Outcome: | This paper examines the analysis and control of text representations, covering methods from pooling to LLM-based. |
Towards a Human-Computer Collaborative Scientific Paper Lifecycle: A Pilot Study and Hands-On Tutorial (2024.lrec-tutorials)
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| Challenge: | a tutorial aims to provide an overview of the scientific paper lifecycle . large language models (LLMs) have increasingly played an important role in academic writing . |
| Approach: | They propose to provide an overview of the scientific paper lifecycle using large language models. |
| Outcome: | The tutorial will provide an overview of the scientific paper lifecycle, including scientific literature understanding, experiment development, manuscript draft writing, and finally draft evaluation. |
Tutorial Proposal: Hallucination in Large Language Models (2024.lrec-tutorials)
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| Challenge: | Grasping the intricacies of hallucination in LLMs can be daunting, especially for those new to the field. |
| Approach: | This tutorial aims to bridge the gap between the field and the field of hallucination . it will explore the key aspects of hallucinonation, including benchmarking, detection, and mitigation techniques . |
| Outcome: | This tutorial will explore the key aspects of hallucination in LLMs . it will also explore the specific constraints and shortcomings of current approaches . |
Addressing Bias and Hallucination in Large Language Models (2024.lrec-tutorials)
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Nihar Ranjan Sahoo, Ashita Saxena, Kishan Maharaj, Arif A. Ahmad, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | This tutorial provides a comprehensive overview of two critical aspects of Large Language Models: bias and hallucination. |
| Approach: | This tutorial provides an overview of two critical aspects of Large Language Models: bias and hallucination. |
| Outcome: | This tutorial delves into the complex dimensions of Large Language Models (LLMs) it outlines ethical considerations pertinent to their development and discusses hallucination, a prevalent issue in generative AI systems such as LLMs. |
Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons (2024.lrec-tutorials)
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| Challenge: | Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language. |
| Approach: | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges. |
| Outcome: | The tutorial aims to give attendees a comprehensive understanding of KEDS, and highlight advances made using deep learning and pinpoint the current challenges. |