Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)
Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond (2023.acl-tutorials)
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| Challenge: | Conventional conversation researches focus on the responseability of the system, such as dialogue context understanding and response generation, but overlook the design of an essential property in intelligent conversations, i.e., goal awareness. |
| Approach: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
| Outcome: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world (2023.acl-tutorials)
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| Challenge: | Responsible AI issues such as fairness, bias and toxicity will be discussed in this tutorial . |
| Approach: | This tutorial will describe various aspects of scaling up language technologies to many of the world’s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). |
| Outcome: | This tutorial will cover various aspects of scaling up language technologies to many of the world's languages by describing the latest research in multilingual models. |
Generating Text from Language Models (2023.acl-tutorials)
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| Challenge: | a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models. |
| Approach: | They will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
| Outcome: | This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
Indirectly Supervised Natural Language Processing (2023.acl-tutorials)
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| Challenge: | a tutorial on indirect supervision addresses challenges in ML for NLP . conventional approaches to NLP use taskspecific labeled examples of a large volume . indirect supervision is useful for a wide range of NLP tasks, but it is not enough for decoders . |
| Approach: | This tutorial aims to address questions about indirect supervision in machine learning . authors discuss indirect supervision from T′ that handles T with outputs spanning from a moderate size to an open space . |
| Outcome: | This tutorial aims to answer questions about how to provide supervision for ML tasks . it will discuss indirect supervision from T′ that handles T with outputs spanning from a moderate size to an open space . |
Retrieval-based Language Models and Applications (2023.acl-tutorials)
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| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
| Approach: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |