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AutoSurvey: Large Language Models Can Automatically Write Surveys
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AutoSurvey: Large Language Models Can Automatically Write Surveys
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This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due to the vast volume and complexity of information, prompting the need for efficient survey methods. While large language models (LLMs) offer promise in automating this process, challenges such as context window limitations, parametric knowledge constraints, and the lack of evaluation benchmarks remain. AutoSurvey addresses these challenges through a systematic approach that involves initial retrieval and outline generation, subsection drafting by specialized LLMs, integration and refinement, and rigorous evaluation and iteration. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.We open our resources at \url{https://github.com/AutoSurveys/AutoSurvey}.
Forward citations
Cited by 13 Pith papers
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
MetaSyn benchmark shows LLM pipelines recover at most 52.7% of ground-truth included studies due to screening failures on PI/ECO eligibility, despite 90.9% retrieval recall at K=200.
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The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
The AI Scientist framework enables LLMs to independently conduct the full scientific process from idea generation to paper writing and review, demonstrated across three ML subfields with papers costing under $15 each.
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
MetaSyn benchmark shows LLM agents recover at most 52.7% of relevant studies in meta-analysis pipelines due to failures in PI/ECO-based screening despite strong retrieval.
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
LLM agents reach 90.9% retrieval recall at K=200 but recover at most 52.7% of ground-truth included studies because they cannot reliably apply PI/ECO eligibility criteria to topically similar distractors.
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
MetaSyn is a stage-level benchmark of 442 meta-analyses showing LLM agents retrieve up to 90.9% of eligible studies but include at most 52.7% in their final reports.
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
RWGBench is a citation-centric benchmark for related work generation built from 40k CS papers and a 100-paper test set, with multi-dimensional metrics that better match human expert judgment than standard similarity scores.
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
RWGBench evaluates related-work generation as citation decision-making (selection, placement, organization, discourse) rather than text similarity, exposing retrieval and generation failures that standard metrics miss.
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
RWGBench measures related-work generation by citation choices, and shows citation-focused metrics expose failures that text-similarity and LLM-judge scores miss.
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DeepSurvey: Enhancing Analytical Depth and Citation Reliability in Automated Survey Generation
DeepSurvey introduces an agentic system for automated survey generation that improves depth through full-text keynotes, cross-paper clustering, and code analysis, while boosting citation reliability via graph expansio...
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
RWGBench evaluates related-work generation as citation decision-making with multi-dimensional metrics that track expert judgment better than ROUGE/BERTScore.
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AI for Auto-Research: Roadmap & User Guide
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AI for Auto-Research: Roadmap & User Guide
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.
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