Re-evaluating four LLM code-efficiency benchmarks with 30-run statistical testing shows 93.89% of 'performant' implementations are indistinguishable from baselines; a multi-agent test-generation framework reveals hidden significant improvements in ~24% of previously non-significant tasks.
Proceedings of the 18th
4 Pith papers cite this work, alongside 36 external citations. Polarity classification is still indexing.
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Rethinking Code Performance Benchmarks for LLMs
Re-evaluating four LLM code-efficiency benchmarks with 30-run statistical testing shows 93.89% of 'performant' implementations are indistinguishable from baselines; a multi-agent test-generation framework reveals hidden significant improvements in ~24% of previously non-significant tasks.
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AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
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RecNextEval: A Reference Implementation for Temporal Next-Batch Recommendation Evaluation
RecNextEval is a reference implementation that applies time-window splits for temporal next-batch recommendation evaluation to minimize data leakage.
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From Query to Conscience: The Importance of Information Retrieval in Empowering Socially Responsible Consumerism
Information retrieval can empower socially responsible consumerism by reducing information asymmetries, supporting complex ethical searches, and calibrating consumer knowledge during product decisions.