Putting Humans in the Natural Language Processing Loop: A Survey

• URL: http://arxiv.org/abs/2103.04044v1
• Date: Sat, 6 Mar 2021 06:26:00 GMT
• Title: Putting Humans in the Natural Language Processing Loop: A Survey
• Authors: Zijie J. Wang, Dongjin Choi, Shenyu Xu, Diyi Yang
• Abstract summary: How can we design Natural Language Processing (NLP) systems that learn from human feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks that continuously integrate human feedback to improve the model itself. We present a survey of HITL NLP work from both Machine Learning (ML) and Human-Computer Interaction (HCI) communities.
• Score: 13.53277201606357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How can we design Natural Language Processing (NLP) systems that learn from human feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks that continuously integrate human feedback to improve the model itself. HITL NLP research is nascent but multifarious -- solving various NLP problems, collecting diverse feedback from different people, and applying different methods to learn from collected feedback. We present a survey of HITL NLP work from both Machine Learning (ML) and Human-Computer Interaction (HCI) communities that highlights its short yet inspiring history, and thoroughly summarize recent frameworks focusing on their tasks, goals, human interactions, and feedback learning methods. Finally, we discuss future directions for integrating human feedback in the NLP development loop.

Related papers

• Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework [13.949126295663328]
  We bridge the gap between machine learning and human-computer interaction efforts by developing a shared understanding of human feedback in interactive learning scenarios. We introduce a taxonomy of feedback types for reward-based learning from human feedback based on nine key dimensions. We identify seven quality metrics of human feedback influencing both the human ability to express feedback and the agent's ability to learn from the feedback.
  arXiv  Detail & Related papers  (2024-11-18T17:40:42Z)
• The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models [0.0]
  Large language models (LLMs) and generative AI have revolutionized natural language processing (NLP) This chapter explores the transformative potential of LLMs in automated question generation and answer assessment.
  arXiv  Detail & Related papers  (2024-10-12T15:54:53Z)
• Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs [31.556095945149583]
  We propose several strategies to improve empathy understanding in language models. A low agreement among annotators hinders training and highlights the subjective nature of the task. To study this, we meticulously collected story pairs in Urdu language and find that subjectivity in interpreting empathy among annotators appears to be independent of cultural background.
  arXiv  Detail & Related papers  (2024-06-17T06:22:20Z)
• Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends [26.90343340881045]
  We review recent advances in NLP with the focus on solving problems relevant to the education domain. We present a taxonomy of NLP in the education domain and highlight typical NLP applications including question answering, question construction, automated assessment, and error correction. We conclude with six promising directions for future research, including more datasets in education domain, controllable usage of LLMs, intervention of difficulty-level control, interpretable educational NLP, methods with adaptive learning, and integrated systems for education.
  arXiv  Detail & Related papers  (2024-01-15T07:48:42Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation [68.9440575276396]
  This survey aims to provide an overview of the recent research that has leveraged human feedback to improve natural language generation. First, we introduce an encompassing formalization of feedback, and identify and organize existing research into a taxonomy following this formalization. Second, we discuss how feedback can be described by its format and objective, and cover the two approaches proposed to use feedback (either for training or decoding): directly using the feedback or training feedback models. Third, we provide an overview of the nascent field of AI feedback, which exploits large language models to make judgments based on a set of principles and minimize the need for
  arXiv  Detail & Related papers  (2023-05-01T17:36:06Z)
• Chain of Hindsight Aligns Language Models with Feedback [62.68665658130472]
  We propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model. By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors.
  arXiv  Detail & Related papers  (2023-02-06T10:28:16Z)
• Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
  Deep learning has been the mainstream technique in natural language processing (NLP) area. Deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
  arXiv  Detail & Related papers  (2022-05-03T13:58:38Z)
• Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19 [58.720142291102135]
  We discuss ways in which current and future NLP approaches can be made more inclusive by covering low-resource languages. We suggest several future directions for researchers interested in maximizing the positive societal impacts of NLP.
  arXiv  Detail & Related papers  (2021-08-11T12:54:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.