A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature

2025-08-06

AI Summary

The rapid proliferation of literature on autoregressive generative models has rendered manual surveying and reproduction increasingly infeasible. To address this, we propose the first end-to-end automated literature curation framework, integrating intelligent retrieval, metadata parsing, thematic clustering, abstractive summarization, and containerized experimental re-execution. Our method innovatively combines relevance classification, named entity recognition, hierarchical clustering, and retrieval-augmented generation (RAG), with reproducible, scalable experimentation enabled via Docker. Evaluated on 50 human-annotated papers, our system achieves an F1 score exceeding 0.85. It supports near-linear processing of thousands of papers and demonstrates high experimental fidelity: three replication studies show perplexity deviations of only 1–3%. This framework significantly enhances research efficiency and verifiability in the field.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The accelerating pace of research on autoregressive generative models has produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical. We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories, filters them for relevance, extracts metadata, hyper-parameters and reported results, clusters topics, produces retrieval-augmented summaries and generates containerised scripts for re-running selected experiments. Quantitative evaluation on 50 manually-annotated papers shows F1 scores above 0.85 for relevance classification, hyper-parameter extraction and citation identification. Experiments on corpora of up to 1000 papers demonstrate near-linear scalability with eight CPU workers. Three case studies -- AWD-LSTM on WikiText-2, Transformer-XL on WikiText-103 and an autoregressive music model on the Lakh MIDI dataset -- confirm that the extracted settings support faithful reproduction, achieving test perplexities within 1--3% of the original reports.

Problem

• Automates literature review for autoregressive model research
• Extracts metadata and results from large paper collections
• Enables reproducible experiments with extracted hyper-parameters

Innovation

• Open-source pipeline for autoregressive model analysis
• Automated document retrieval and relevance filtering
• Containerized scripts for experiment reproduction