Long-Context Retrieval Evaluation Task

A Long-Context Retrieval Evaluation Task is a benchmark information retrieval task that can support context window assessments by measuring retrieval accuracy across extended token sequences.

• AKA: Needle-in-Haystack Test, Long-Context Needle Test, Extended Context Retrieval Task, Token Window Evaluation.
• Context:
  • It can typically embed Target Informations through strategic placements within long documents.
  • It can typically measure Retrieval Accuracys through exact match scorings.
  • It can typically vary Needle Positions through systematic distributions.
  • It can typically scale Context Lengths through incremental expansions.
  • It can typically assess Attention Mechanisms through position-based analysises.
  • ...
  • It can often test Multi-Needle Retrievals through complex querys.
  • It can often evaluate Cross-Document References through relationship trackings.
  • It can often measure Degradation Patterns through performance curves.
  • It can often identify Attention Limits through failure analysises.
  • ...
  • It can range from being a Simple Long-Context Retrieval Evaluation Task to being a Complex Long-Context Retrieval Evaluation Task, depending on its query sophistication level.
  • It can range from being a Short Long-Context Retrieval Evaluation Task to being an Extended Long-Context Retrieval Evaluation Task, depending on its maximum token count.
  • It can range from being a Single-Needle Long-Context Retrieval Evaluation Task to being a Multi-Needle Long-Context Retrieval Evaluation Task, depending on its target information count.
  • It can range from being a Synthetic Long-Context Retrieval Evaluation Task to being a Natural Long-Context Retrieval Evaluation Task, depending on its document source type.
  • ...
  • It can integrate with Benchmark Suites for comprehensive evaluation.
  • It can connect to Visualization Tools for performance mapping.
  • It can interface with Statistical Analysises for significance testing.
  • It can communicate with Model Comparison Frameworks for relative assessment.
  • It can synchronize with Leaderboard Systems for ranking updates.
  • ...
• Example(s):
  • Standard Needle Tests, such as:
    • Paul Graham Essay Test with fact retrieval.
    • Wikipedia Article Test with entity extraction.
  • Scaled Context Tests, such as:
    • 128K Token Retrieval Test for GPT-4 Turbo evaluation.
    • 400K Token Retrieval Test for GPT-5 evaluation.
    • 1M Token Retrieval Test for Gemini evaluation.
  • Domain-Specific Tests, such as:
    • Legal Document Retrieval for contract analysis.
    • Code Repository Search for function finding.
  • Multi-Modal Tests, such as:
    • Image-Text Retrieval across long conversations.
    • Audio Transcript Search in extended dialogues.
  • ...
• Counter-Example(s):
  • Short-Context Task, which uses limited token windows.
  • Generation Task, which focuses on content creation rather than retrieval.
  • Classification Task, which categorizes rather than retrieves information.
• See: Context Window, Attention Mechanism, Information Retrieval Task, Benchmark Evaluation, OpenAI GPT-5 Language Model, Position Encoding, Memory Management, Transformer Architecture, Long-Context Language Model.

---