Context window evaluation and truncation labeling

*Overview This service helps AI teams understand and improve how models handle extended context windows. Many LLMs struggle with retaining key information when inputs exceed their token limit, leading to degraded reasoning or incomplete answers. Our human reviewers evaluate outputs against the full input, label truncation effects, and provide actionable insights for fine-tuning or architecture adjustments. How it Works You provide datasets containing long-form prompts, reference outputs, or model responses. We process them through a structured annotation workflow: Context integrity check against the full prompt Truncation point identification and mapping Scoring dropped vs. retained content by relevance and impact Flagging and categorizing degraded reasoning caused by truncation Reviewer consensus and adjudication for edge cases Automated token-level diff and overlap analysis Packaging of annotations into benchmarking datasets and reports Deliverables Per engagement, we provide: Annotated dataset (JSONL/CSV) with truncation flags, retained/dropped content tags, and impact scores Summary report with metrics on context loss, degradation rates, and affected reasoning steps Heatmaps showing token retention vs. cut-off points Corrective modeling suggestions for long-context handling Reviewer notes on ambiguity or borderline cases Quality & Metrics We track key indicators, including: Context retention percentage Critical information loss rate Impact severity score (qualitative + quantitative) Inter-annotator agreement for truncation labeling Per-category degradation metrics (factual, logical, narrative coherence) Integrations & Formats Outputs are delivered in JSONL, CSV, or SageMaker Ground Truth-compatible manifests. Easily integrate findings into: Long-context optimization pipelines Token management and compression strategies Evaluation scripts for long-form QA and summarization Security & Compliance Data is processed under encrypted storage, private S3 buckets, and role-based access protocols. Optional compliance packages available for regulated industry datasets. Engagement Models One-time context window audit: For model release readiness Ongoing truncation monitoring: Continuous evaluation for production models Fine-tuning dataset creation: Long-context retention improvement through labeled samples *