Context-Length Robustness in Question Answering Models: A Comparative Empirical Study
#question answering #context length #robustness #empirical study #benchmarking #AI models #performance comparison
📌 Key Takeaways
- The study compares question answering models' performance across varying context lengths.
- It identifies models that maintain accuracy with longer input contexts versus those that degrade.
- Findings highlight architectural features influencing robustness to extended contexts.
- The research provides benchmarks for evaluating context-length handling in QA systems.
📖 Full Retelling
arXiv:2603.15723v1 Announce Type: new
Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, robustness to growing context length remains poorly understood across different question answering tasks. In this work, we present a controlled empirical study of context-length robustness in large language models using two widely used benchmarks: SQuAD and HotpotQA.
We evaluate model accuracy as a func
🏷️ Themes
AI Robustness, Model Evaluation
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Original Source
arXiv:2603.15723v1 Announce Type: new
Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, robustness to growing context length remains poorly understood across different question answering tasks. In this work, we present a controlled empirical study of context-length robustness in large language models using two widely used benchmarks: SQuAD and HotpotQA.
We evaluate model accuracy as a func
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