A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature

• URL: http://arxiv.org/abs/2508.04612v1
• Date: Wed, 06 Aug 2025 16:33:20 GMT
• Title: A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature
• Abstract summary: Autoregressive generative models have produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical.<n>We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories.<n>Experiments on corpora of up to 1000 papers demonstrate near-linear scalability with eight CPU workers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Prompt-to-Paper: Agentic AI System for Bioinformatics [0.0]
  We present Prompt-to-Paper, a multi-agent framework that directly addresses this evaluation gap through three integrated innovations.<n>First, a deterministic retrieval-augmented generation pipeline with section-aware relevance scoring and snowball citation expansion grounds every claim in a verifiable corpus of 60--100 papers.<n>Second, an autonomous coding agent executes real computational biology experiments replacing synthetic outputs with genuine numerical results.<n>Third, an eight-dimensional automated quality scorer, benchmarked with approximate reference statistics from published papers and augmented with explicit hallucination penalties, provides standardized, reproducible quality assessments.
  arXiv  Detail & Related papers  (2026-07-05T21:30:24Z)
• MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation [64.33427079838602]
  Retrieval-augmented generation from videos requires systems to retrieve relevant audiovisual evidence from large corpora and synthesize it into coherent, attributed text.<n>We present MARQUIS: a three-stage pipeline that addresses these limitations through query expansion, fusion, and reranking.<n>For article generation, ITER-QA-BASE improves average human score from 3.09 to 3.83 over the CAG baseline, while MARQUIS-RLM achieves a human score of 3.30 and the strongest citation recall among non-QA systems.
  arXiv  Detail & Related papers  (2026-05-17T20:19:04Z)
• sui-1: Grounded and Verifiable Long-Form Summarization [0.10888485668490162]
  Large language models generate plausible but unfaithful summaries that users cannot verify against source text.<n>We present sui-1, a 24B parameter model that produces abstractive summaries with inline citations.<n>Our synthetic data pipeline combines chain-of-thought prompting with multi-stage verification, generating over 22,000 high-quality training examples.
  arXiv  Detail & Related papers  (2026-01-13T11:59:15Z)
• AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research [81.04845910798387]
  Generating natural language explanations for threat detections remains an open problem in cybersecurity research.<n>We present AutoMalDesc, an automated static analysis summarization framework that operates independently at scale.<n>We publish our complete dataset of more than 100K script samples, including annotated seed (0.9K) datasets, along with our methodology and evaluation framework.
  arXiv  Detail & Related papers  (2025-11-17T13:05:25Z)
• ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval [46.111605335278746]
  ReasonEmbed is a novel text embedding model developed for reasoning-intensive document retrieval.<n>ReMixer is a new data synthesis method that overcomes the triviality problem prevalent in previous synthetic datasets.<n>Redapter is a self-adaptive learning algorithm that dynamically adjusts training each sample's weight based on its reasoning intensity.
  arXiv  Detail & Related papers  (2025-10-09T14:10:26Z)
• Extract-0: A Specialized Language Model for Document Information Extraction [0.0]
  This paper presents Extract-0, a 7-billion parameter language model specifically optimized for document information extraction.<n>Extract-0 achieves a mean reward of 0.573 on a benchmark of 1,000 diverse document extraction tasks, outperforming GPT-4.1 (0.457), o3 (0.464), and GPT-4.1-2025 (0.459).
  arXiv  Detail & Related papers  (2025-09-26T20:34:43Z)
• Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search [54.987957691350665]
  Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query.<n>Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications.<n>We propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search.
  arXiv  Detail & Related papers  (2025-08-28T08:51:51Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models [53.17363502535395]
  Trustworthy language models should provide both correct and verifiable answers.<n>Current systems insert citations by querying an external retriever at inference time.<n>We propose Active Indexing, which continually pretrains on synthetic QA pairs.
  arXiv  Detail & Related papers  (2025-06-21T04:48:05Z)
• MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs [54.5729817345543]
  MOLE is a framework that automatically extracts metadata attributes from scientific papers covering datasets of languages other than Arabic.<n>Our methodology processes entire documents across multiple input formats and incorporates robust validation mechanisms for consistent output.
  arXiv  Detail & Related papers  (2025-05-26T10:31:26Z)
• Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention [6.938202451113495]
  We present a novel framework that combines an extraction model based on MatSciBERT with pointer and an allocation model.<n>Our experiments on extraction demonstrate impressive F1 scores of 0.947, 0.93 and 0.753 across datasets.<n>These results highlight the model's capacity to deliver precise and structured information.
  arXiv  Detail & Related papers  (2025-03-10T02:39:06Z)
• BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression [91.23933111083389]
  Retrieval-augmented generation (RAG) can supplement large language models (LLMs) by integrating external knowledge.<n>This paper presents BRIEF, a lightweight approach that performs query-aware multi-hop reasoning.<n>Based on our synthetic data built entirely by open-source models, BRIEF generates more concise summaries.
  arXiv  Detail & Related papers  (2024-10-20T04:24:16Z)
• REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models [14.023953508288628]
  Retrieval augmented generation (RAG) pipelines are commonly used in tasks such as question-answering (QA) We propose REFINE, a novel technique that generates synthetic data from available documents and then uses a model fusion approach to fine-tune embeddings.
  arXiv  Detail & Related papers  (2024-10-16T08:43:39Z)
• Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities [2.7311827519141363]
  We propose Text to Topic (Text2Topic), which achieves high multi-label classification performance. Text2Topic supports zero-shot predictions, produces domain-specific text embeddings, and enables production-scale batch-inference. The model is deployed on a real-world stream processing platform, and it outperforms other models with 92.9% micro mAP.
  arXiv  Detail & Related papers  (2023-10-23T11:33:24Z)
• How Does Generative Retrieval Scale to Millions of Passages? [68.98628807288972]
  We conduct the first empirical study of generative retrieval techniques across various corpus scales. We scale generative retrieval to millions of passages with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters. While generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge.
  arXiv  Detail & Related papers  (2023-05-19T17:33:38Z)
• BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection [63.447493500066045]
  This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects.
  arXiv  Detail & Related papers  (2022-07-27T09:26:15Z)
• Learning Summary Statistics for Bayesian Inference with Autoencoders [58.720142291102135]
  We use the inner dimension of deep neural network based Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information that has been used to generate the training data.
  arXiv  Detail & Related papers  (2022-01-28T12:00:31Z)
• The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild [0.0]
  The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 0.22, while Transformer-based models achieve scores of around 0.58, and (2) multilingual Transformer models work as well on the task as the monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks.
  arXiv  Detail & Related papers  (2022-01-11T09:39:15Z)

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.