Joint Retrieval and Generation Training for Grounded Text Generation

• URL: http://arxiv.org/abs/2105.06597v1
• Date: Fri, 14 May 2021 00:11:38 GMT
• Title: Joint Retrieval and Generation Training for Grounded Text Generation
• Authors: Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan
• Abstract summary: Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data. We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. We demonstrate that by taking advantage of external references our approach can produce more informative and interesting text in both prose and dialogue generation.
• Score: 75.11057157342974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from problems of hallucinated facts, and are not inherently designed to incorporate useful external information. Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data where corresponding documents are provided for context. We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. The model learns to retrieve the documents with the highest utility in generation and attentively combines them in the output. We demonstrate that by taking advantage of external references our approach can produce more informative and interesting text in both prose and dialogue generation.

Related papers

• SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation [55.61004653386632]
  Large Language Models (LLMs) often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. This paper introduces a novel self-supervised method for generating a training set of unfaithful samples. We then refine the model using a training process that encourages the generation of grounded outputs over unfaithful ones.
  arXiv  Detail & Related papers  (2025-02-19T12:31:58Z)
• Analysis of Plan-based Retrieval for Grounded Text Generation [78.89478272104739]
  hallucinations occur when a language model is given a generation task outside its parametric knowledge. A common strategy to address this limitation is to infuse the language models with retrieval mechanisms. We analyze how planning can be used to guide retrieval to further reduce the frequency of hallucinations.
  arXiv  Detail & Related papers  (2024-08-20T02:19:35Z)
• Text2Data: Low-Resource Data Generation with Textual Control [100.5970757736845]
  Text2Data is a novel approach that utilizes unlabeled data to understand the underlying data distribution. It undergoes finetuning via a novel constraint optimization-based learning objective that ensures controllability and effectively counteracts catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-02-08T03:41:39Z)
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy [164.83371924650294]
  We show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge. Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.
  arXiv  Detail & Related papers  (2023-05-24T16:17:36Z)
• MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective [22.69509556890676]
  We propose a novel multi-task training strategy for coherent text generation grounded on the cognitive theory of writing. We extensively evaluate our model on three open-ended generation tasks including story generation, news article writing and argument generation.
  arXiv  Detail & Related papers  (2022-10-26T11:55:41Z)
• Automatic Document Sketching: Generating Drafts from Analogous Texts [44.626645471195495]
  We introduce a new task, document sketching, which involves generating entire draft documents for the writer to review and revise. These drafts are built from sets of documents that overlap in form - sharing large segments of potentially reusable text - while diverging in content. We investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-14T06:46:06Z)
• POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
  We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2020-05-01T18:11:54Z)

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.