Business Context
You’re joining HelioBio, a biotech intelligence platform used by 40+ pharma companies to monitor emerging evidence in oncology. The product ingests ~120,000 new PDFs/month from PubMed Central, preprint servers, and conference proceedings. Customers ask questions like “What evidence supports combining PARP inhibitors with immunotherapy in ovarian cancer?” and expect answers grounded in specific passages.
A major blocker is that many papers are 20–60 pages (often 10k–40k tokens after extraction), far exceeding the 8k–16k token context windows of the LLMs HelioBio can run in its HIPAA/GxP-compliant environment. If the system truncates, it misses critical details (inclusion criteria, endpoints, adverse events), leading to incorrect summaries that can affect research prioritization and millions of dollars in downstream decisions.
Data Characteristics
- Corpus: 18 months of biomedical literature (oncology focus), ~2.1M sections after parsing
- Text length:
- Abstracts: 150–400 words
- Sections: 300–2,500 words (Methods can be longer)
- Full papers: median 18k tokens; p95 38k tokens
- Structure: Titles, headings, tables/figures captions, references; noisy PDF extraction (hyphenation, broken lines)
- Language: 96% English, 4% mixed (English + Latin terms / gene symbols)
- Domain vocabulary: gene/protein symbols (BRCA1, PD-1), trial IDs (NCT...), statistical terms (HR, CI), dosing schedules
Success Criteria
- Answer quality: For a held-out set of 1,000 expert-written Q/A pairs, achieve ≥0.75 citation precision (citations truly support the claim) and ≥0.70 answer completeness (covers key points in rubric).
- Grounding: Every non-trivial claim must include at least one citation (section + sentence span).
- Latency: p95 < 2.5s per question (excluding PDF ingestion), on a single A10 GPU + CPU workers.
- Cost: Average < 6,000 generated tokens per query.
Constraints
- No external API calls; models run in a private VPC.
- Must support document-level reasoning across long papers without exceeding context.
- Must be robust to PDF extraction artifacts and section boundary errors.
Requirements (Deliverables)
- Propose a strategy to handle context window limits when answering questions over long scientific papers.
- Design a chunking + indexing approach that preserves scientific structure (e.g., Methods vs Results) and supports citation.
- Describe how you would do hierarchical summarization (section → paper → multi-paper) without losing key details.
- Provide an implementation outline (Python) using
transformers + spaCy for preprocessing and a vector index for retrieval.
- Define an evaluation plan: offline metrics, human review protocol, and error analysis focused on truncation/retrieval failures.
Your solution should explicitly discuss trade-offs between: (a) larger-context models vs retrieval, (b) chunk size/overlap vs recall/latency, and (c) extractive vs abstractive summarization for scientific claims.
