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This paper presents a rigor-matched audit comparing periodic-step layer-skipping methods like ConfLayers and SWIFT for efficient LLM inference, and analyzes trained routing alternatives, finding SWIFT superior in accuracy and true inference speed.
This paper audits 14 large language models for hiring discrimination using a paired-resume methodology, finding that older models exhibit pro-White bias while newer models show null or pro-Black bias, indicating a reversal in algorithmic hiring bias across model generations.
This paper presents a large-scale audit of recommendation biases in LLM-based content curation across OpenAI, Anthropic, and Google using 540,000 simulated selections from Twitter/X, Bluesky, and Reddit data. The study finds that LLMs systematically amplify polarization, exhibit distinct toxicity handling trade-offs, and show significant political leaning bias favoring left-leaning authors despite right-leaning plurality in datasets.