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A paper proposes SRBench, a benchmark framework targeting the screening phase of systematic reviews, along with a companion experimental tool PromptSR for evaluating large language models on literature screening tasks.
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9/28/2026
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UnverifiedThe study employed a combination of staged screening and automated full-text coding to analyze the papers.71% similarUnverifiedPromptSR provides three capabilities — prompt experimentation, experiment management, and result analysis — to support LLM-based screening experiments.69% similarUnverifiedThe study conducts experiments on ForecastBench-style binary prediction tasks, examining retrieved evidence, reasoning, reference market priors, and invocation of historical analogies as observable behaviors.66% similarUnverifiedStructuring screening criteria into templates such as the PICO framework (Population, Intervention, Comparison, Outcome) and embedding them in prompts elicits more accurate model judgments than simple natural-language instructions.65% similarUnverifiedSWE-bench involves high-level capabilities such as cross-file dependency analysis, version compatibility judgment, and regression test passing, making it more reflective of real engineering scenarios than function-writing benchmarks such as HumanEval64% similar
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