5 papers
Which Long Documents Matter? A Controlled Study of Data Selection for Context Extension
Junlong Jia, Jiang Zhou, Xing Wu +3
Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear. We conduct a controlled comparison on Qwen2.5-3B at 128K…
PolicyLong: Towards On-Policy Context Extension
Junlong Jia, Jiang Zhou, Ziyang Chen +5
Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependencies via information-theoretic ve…
LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
Ziyang Chen, Xing Wu, Junlong Jia +4
The rapid expansion of context length in large language models (LLMs) has outpaced existing evaluation benchmarks. Current long-context benchmarks often trade off scalability and r…
EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Junlong Jia, Ziyang Chen, Xing Wu +5
Training long-context language models to capture long-range dependencies requires specialized data construction. Current approaches, such as generic text concatenation or heuristic…
LiteLong: Resource-Efficient Long-Context Data Synthesis for LLMs
Junlong Jia, Xing Wu, Chaochen Gao +8
High-quality long-context data is essential for training large language models (LLMs) capable of processing extensive documents, yet existing synthesis approaches using relevance-b…