Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods

• URL: http://arxiv.org/abs/2203.05227v1
• Date: Thu, 10 Mar 2022 08:28:32 GMT
• Title: Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
• Authors: Wei Li, Wenhao Wu, Moye Chen, Jiachen Liu, Xinyan Xiao, Hua Wu
• Abstract summary: Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. However, the faithfulness problem that the generated text usually contains unfaithful or non-factual information has become the biggest challenge.
• Score: 48.47413103662829
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. This advancement has resulted in more fluent, coherent and even properties controllable (e.g. stylistic, sentiment, length etc.) generation, naturally leading to development in downstream tasks such as abstractive summarization, dialogue generation, machine translation, and data-to-text generation. However, the faithfulness problem that the generated text usually contains unfaithful or non-factual information has become the biggest challenge, which makes the performance of text generation unsatisfactory for practical applications in many real-world scenarios. Many studies on analysis, evaluation, and optimization methods for faithfulness problems have been proposed for various tasks, but have not been organized, compared and discussed in a combined manner. In this survey, we provide a systematic overview of the research progress on the faithfulness problem of NLG, including problem analysis, evaluation metrics and optimization methods. We organize the evaluation and optimization methods for different tasks into a unified taxonomy to facilitate comparison and learning across tasks. Several research trends are discussed further.

Related papers

• Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
  Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
  arXiv  Detail & Related papers  (2024-07-04T16:41:08Z)
• A Survey on Natural Language Counterfactual Generation [7.022371235308068]
  Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. We propose a new taxonomy that systematically categorizes the generation methods into four groups and summarizes the metrics for evaluating the generation quality.
  arXiv  Detail & Related papers  (2024-07-04T15:13:59Z)
• Challenges and Opportunities in Text Generation Explainability [12.089513278445704]
  This paper outlines 17 challenges categorized into three groups that arise during the development and assessment of explainability methods. These challenges encompass issues concerning tokenization, defining explanation similarity, determining token importance and prediction change metrics, the level of human intervention required, and the creation of suitable test datasets. The paper illustrates how these challenges can be intertwined, showcasing new opportunities for the community.
  arXiv  Detail & Related papers  (2024-05-14T09:44:52Z)
• A Systematic Review of Data-to-Text NLG [2.4769539696439677]
  Methods for producing high-quality text are explored, addressing the challenge of hallucinations in data-to-text generation. Despite advancements in text quality, the review emphasizes the importance of research in low-resourced languages.
  arXiv  Detail & Related papers  (2024-02-13T14:51:45Z)
• Multi-Dimensional Evaluation of Text Summarization with In-Context Learning [79.02280189976562]
  In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization. We then analyze the effects of factors such as the selection and number of in-context examples on performance.
  arXiv  Detail & Related papers  (2023-06-01T23:27:49Z)
• Visualizing the Relationship Between Encoded Linguistic Information and Task Performance [53.223789395577796]
  We study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality. We conduct experiments on two popular NLP tasks, i.e., machine translation and language modeling, and investigate the relationship between several kinds of linguistic information and task performances. Our empirical findings suggest that some syntactic information is helpful for NLP tasks whereas encoding more syntactic information does not necessarily lead to better performance.
  arXiv  Detail & Related papers  (2022-03-29T19:03:10Z)
• A Survey on Retrieval-Augmented Text Generation [53.04991859796971]
  Retrieval-augmented text generation has remarkable advantages and has achieved state-of-the-art performance in many NLP tasks. It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks.
  arXiv  Detail & Related papers  (2022-02-02T16:18:41Z)
• Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions [4.409836695738517]
  We present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deep-dive into the various dimensions of robustness, across techniques, metrics, embeddings, and benchmarks.
  arXiv  Detail & Related papers  (2022-01-03T17:17:11Z)
• Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers [8.19984844136462]
  We present and evaluate a text generation method suitable to increase the performance of classifiers for long and short texts. In a simulated low data regime additive accuracy gains of up to 15.53% are achieved. We discuss implications and patterns for the successful application of our approach on different types of datasets.
  arXiv  Detail & Related papers  (2021-03-26T13:16:07Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)

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.