Disclaimer: this is a report generated with my tool: https://github.com/DTeam-Top/tsw-cli. See it as an experiment not a formal research, 😄。
Mindmap
Summary
This paper introduces STORM, a novel writing system that leverages large language models (LLMs) to automate the creation of Wikipedia-like articles from scratch. STORM addresses the pre-writing stage, which includes researching the topic and preparing an outline, by using a multi-perspective question-asking approach. The system's effectiveness is evaluated using a newly curated dataset, FreshWiki, and feedback from experienced Wikipedia editors, demonstrating improvements in article organization and coverage compared to existing methods.
Terminology
- LLM (Large Language Model): A deep learning model trained on a massive dataset of text to generate human-like text.
- RAG (Retrieval-Augmented Generation): A framework that enhances text generation by retrieving relevant information from external sources and incorporating it into the generated text.
- FreshWiki: A dataset of recent, high-quality Wikipedia articles curated to avoid data leakage during LLM pre-training.
- Pre-writing: The initial stage of the writing process, which involves researching the topic, gathering information, and creating an outline.
- STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking): A writing system that automates the pre-writing stage by discovering diverse perspectives, simulating conversations with a topic expert, and curating the collected information to create an outline.
Main Points
Point 1: The Challenge of Generating Wikipedia-Like Articles from Scratch
Generating comprehensive and well-organized articles like Wikipedia pages requires more than just fluent writing. It demands thorough research and planning in the pre-writing stage, a process often bypassed in previous studies. This paper addresses this gap by focusing on automating the entire process, including the crucial pre-writing phase.
Explanation:
Previous approaches often assumed the availability of reference documents or outlines, neglecting the information literacy skills needed to identify, evaluate, and organize external sources. Automating this process can empower individuals to learn in-depth about a topic and reduce the expert hours required for expository writing.
Point 2: STORM: A Multi-Perspective Question-Asking Approach
STORM automates the pre-writing stage through a novel approach that involves:
- Discovering Diverse Perspectives: Identifying various viewpoints related to the topic by analyzing existing articles from similar subjects.
- Simulating Conversations: Creating multi-turn conversations where LLMs, personified with specific perspectives, ask questions to a topic expert grounded on trusted Internet sources.
- Curating Information: Synthesizing the collected information and leveraging the LLM's internal knowledge to create a detailed article outline. Implementation: The process starts with identifying related Wikipedia articles to extract tables of contents, which are then used to prompt the LLM to identify N perspectives. Each perspective guides the LLM in asking questions. The LLM breaks down complex questions into search queries, filters search results based on Wikipedia guidelines, and synthesizes trustworthy sources to generate answers. Finally, the LLM refines a draft outline based on the simulated conversations, resulting in an improved outline used for producing the full-length article.
Point 3: Evaluation with FreshWiki and Expert Feedback
The effectiveness of STORM is evaluated using the FreshWiki dataset and feedback from experienced Wikipedia editors. The evaluation includes:
- Outline Quality Assessment: Measuring the coverage of the generated outline using metrics like heading soft recall and heading entity recall, which compare the outline's section headings to those of human-written articles.
- Article Quality Assessment: Assessing the quality of the full-length article using ROUGE scores, entity recall, and a 5-point rubric (developed with Wikipedia editors) to evaluate aspects like interest level, coherence, relevance, coverage, and verifiability. Explanation: The FreshWiki dataset mitigates data leakage by using recent, high-quality Wikipedia articles created after the training cutoff of the LLMs. Expert evaluations from Wikipedia editors provide qualitative insights into the strengths and weaknesses of STORM, highlighting areas for future improvement.
Improvements And Creativity
- Automating the Pre-writing Stage: STORM's primary innovation is its focus on automating the pre-writing phase, a critical but often overlooked aspect of long-form article generation.
- Multi-Perspective Question Asking: The system's use of diverse perspectives to guide question asking allows for more comprehensive and in-depth research.
- FreshWiki Dataset: The creation of the FreshWiki dataset addresses the issue of data leakage and provides a more reliable benchmark for evaluating LLMs in Wikipedia-like article generation.
- Outline-Driven Approach: By explicitly generating and refining an outline before writing the full article, STORM mirrors the human writing process and improves article structure and organization.
Insights
STORM represents a significant step towards automating the creation of high-quality, grounded articles. However, the expert feedback reveals several challenges that warrant further research:
- Bias Mitigation: Reducing the transfer of bias and non-neutral tone from Internet sources to the generated articles.
- Verifiability Enhancement: Addressing the issue of "red herring fallacy" or over-association of unrelated facts, which requires more than just fact-checking.
- Multi-Modal Content Generation: Extending the system to generate structured data (e.g., tables) and multi-modal information, which are common in human-authored Wikipedia articles.
- Improve the Retrieval Module: Improving the retrieval module to have good coverage of different viewpoints and adding a content sifting module to the current system will be a critical next step to achieve better neutrality and balance in the generated articles.
References
Paper: STORM: Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking
Report generated by TSW-X
Advanced Research Systems Division
Date: 2025-03-09 09:38:37.892656
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