Generating Informative and Verifiable Summaries with Citation Support

核心概念

Plan-based models that generate text with citations improve the faithfulness, grounding, and controllability of the generated output compared to standard sequence-to-sequence models.

摘要

The paper explores the attribution capabilities of plan-based models for long-form question answering and query-focused summarization tasks. The authors propose two variants of blueprint models - an abstractive model where questions are generated from scratch, and an extractive model where the decoder is forced to copy questions from the input.

The key highlights are:

• Blueprint models consistently improve attribution quality compared to standard sequence-to-sequence models.
• The extractive blueprint model performs best across evaluation metrics, achieving a new state-of-the-art on the AQuAMuSe dataset.
• Blueprint models generate more abstractive summaries compared to the baseline, indicating they consolidate information from multiple sources.
• Different citation formats are explored, with in-line citations referencing both passages and blueprint questions showing the best performance.
• The blueprint models demonstrate robust transfer of attribution skills to out-of-domain datasets, outperforming large language model-based pipelines.

統計資料

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引述

"The increasing demand for the deployment of LLMs in information-seeking scenarios has further spurred efforts in creating verifiable systems, which generate responses to queries along with supporting evidence."
"Despite recent efforts, it remains an open question how to best develop models with a built-in mechanism for attribution to external evidence."

從以下內容提煉的關鍵洞見

Learning to Plan and Generate Text with Citations

by Constanza Fi... 於 arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03381.pdf

深入探究

How can plan-based models be further improved to better ground the generated text to the input passages?

Plan-based models can be enhanced to better ground the generated text to the input passages by incorporating more sophisticated mechanisms for aligning the generated content with the source material. One approach could involve refining the question generation process to ensure that the questions in the blueprint are closely tied to the information present in the input passages. This alignment can be strengthened by implementing more advanced techniques for question-answer pair extraction and verification. Additionally, introducing a feedback loop mechanism that iteratively refines the blueprint based on the input passages can help improve the grounding of the generated text. By continuously comparing the generated output with the input passages and adjusting the blueprint accordingly, the model can ensure a more accurate and coherent representation of the source information in the final text.

What are the potential limitations of the proposed blueprint models, and how could they be addressed?

One potential limitation of blueprint models is the reliance on the quality of the input passages and the accuracy of the question generation process. If the input passages are noisy or irrelevant, it can impact the effectiveness of the blueprint in guiding the text generation. Similarly, inaccuracies in the question generation phase can lead to misalignment between the blueprint and the input passages, resulting in a lack of grounding in the generated text. To address these limitations, it is essential to implement robust preprocessing steps to filter out irrelevant passages and ensure the accuracy of the question generation models. Additionally, incorporating a mechanism for dynamic adjustment of the blueprint based on the content of the input passages can help mitigate the impact of noisy or inaccurate information.

How could the insights from this work be applied to other text generation tasks beyond question answering and summarization?

The insights from this work on plan-based models and attribution mechanisms can be applied to a wide range of text generation tasks beyond question answering and summarization. For instance, in content generation for educational purposes, blueprint models can be utilized to provide structured and informative explanations for complex concepts. By incorporating attribution mechanisms, the generated content can be enriched with references to authoritative sources, enhancing the credibility and trustworthiness of the information. Similarly, in content creation for news articles or reports, blueprint models can help ensure that the generated text is well-organized and supported by relevant evidence. By adapting the blueprint framework and attribution techniques to different domains and tasks, text generation models can produce more coherent, informative, and verifiable content across various applications.