| Challenge: | Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. |
| Approach: | This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process. |
| Outcome: | This tutorial summarizes the main AutoML techniques and illustrates how to apply them to improve the model-building process. |
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| Challenge: | tutorial addresses three critical questions within the field of instruction tuning: (1) What are the current focal points in instruction tuning research? (2) What are best practices in training an instruction-following model? (3) What new challenges have emerged? |
| Approach: | This tutorial presents a systematic overview of recent advances in instruction tuning. |
| Outcome: | The tutorial covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. |
A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
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| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
| Outcome: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
| Outcome: | This tutorial aims to answer questions about instruction-driven NLP . it focuses on the use of task instructions in a low-shot scenario . |
Beyond Paragraphs: NLP for Long Sequences (2021.naacl-tutorials)
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| Challenge: | In this tutorial, we will introduce document-level representation learning techniques . document-based learning is challenging due to the limited sequence length of many models . |
| Approach: | They will provide an overview of established long sequence NLP techniques and discuss memory-saving methods that are key to processing long sequences. |
| Outcome: | The tutorial will introduce the latest and ongoing techniques for document-level representation learning. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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| Challenge: | a new study examines the current state of knowledge about the BERT model . the model is a stack of transformer encoder layers that are based on multiple self-attention ''heads'' |
| Approach: | They present a survey of over 150 studies of the popular Transformer-based model BERT . they discuss the current state of knowledge about how BERT works and how it is represented . |
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Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
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| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
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Deep Learning for Natural Language Inference (N19-5)
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Modular and Parameter-Efficient Fine-Tuning for NLP Models (2022.emnlp-tutorials)
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| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
| Approach: | They provide an overview of parameter-efficient fine-tuning methods and highlight similarities and differences . they highlight benefits and usage scenarios of a neglected property of parameter efficient models . |
| Outcome: | This paper provides an overview of parameter-efficient fine-tuning methods . it highlights similarities and differences by presenting them in a unified view . |
Adaptive Testing and Debugging of NLP Models (2022.acl-long)
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| Challenge: | Current approaches to testing and debugging NLP models rely on variable human creativity and extensive labor to instantiate bugs. |
| Approach: | They propose a process which uses large scale language models to automatically write unit tests highlighting bugs in a target model. |
| Outcome: | The proposed process makes users 5-10x more effective at finding bugs than current approaches. |