Context-Length Robustness in Question Answering Models: A Comparative Empirical

Abstract

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 function of total context length by systematically increasing the amount of irrelevant context while preserving the answer-bearing signal. This allows us t

Research Question

How does the robustness of GPT-4's multi-hop reasoning degrade under increasing retrieval steps (2 vs 5) on the Cofca benchmark, and does the accuracy-throughput trade-off favor wider context windows or multi-step retrieval pipelines?

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