Agentic Long Context Understanding QA: Self-Taught Query Refinement

AgenticLU is a dataset for evaluating long-context understanding in language models, created by yzhuang and last updated on September 22, 2025. It contains queries refined through self-clarifications and contextual grounding to enable robust long-document understanding in a single pass. The dataset is hosted on the Hugging Face platform.

Use Cases

• Benchmarking long-context question-answering models based on the described self-clarification queries.
• Training agentic systems to refine complex queries based on the described contextual grounding mechanism.
• Evaluating single-pass understanding of long documents based on the dataset's stated purpose.

Strengths

• Focuses on agentic long-context understanding, a specific and advanced NLP task.
• Dataset was last updated on 2025-09-22, indicating recent maintenance.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

yzhuang on Hugging Face, based on research from Arxiv.

Collection Method

Likely created for research on self-taught agentic long-context understanding.

Freshness

Last updated 2025-09-22 17:22:55.