Hi everyone,
I’ve been working on optimizing RAG pipelines and LLM workflows that are specifically designed to process dense, domain-specific academic literature and scientific text.
One consistent roadblock I keep encountering is the massive drop in model recall when feeding LLMs text data derived from generic web scrapers or poorly parsed PDFs. When a pipeline relies on loose scraping, the text loses its underlying structural integrity—things like precise citation mapping, tabular data alignment, and nested metadata graphs break down. Even with long-context models, if the input layer is noisy or un-indexed, the model’s attention system dilutes, leading to hallucinations or complete omissions during reasoning tasks.
To counter this in our architecture, we’ve shifted completely away from unstructured text dumps toward feeding models machine-readable JSON metadata right at the ingestion layer. For our structured academic data infrastructure, we have been leveraging ScholarAPI to fetch clean, pre-indexed metadata and full-text access. Making the data ingestion layer deterministic this way has heavily optimized our evaluation accuracy and saved massive engineering overhead.
I’d love to know the community’s thoughts on this:
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What data-cleaning strategies or specialized data gateways do you use when training/evaluating models on complex scientific datasets?
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How are you maintaining the structural integrity of reference data inside your context windows to avoid attention dilution?
Looking forward to a great technical discussion!