Mitigating LLM biases toward spurious social contexts using direct preference optimization explores A novel training method significantly reduces LLM bias towards spurious social contexts in high-stakes decision-making, improving both accuracy and robustness.. Commercial viability score: 7/10 in LLM Bias Mitigation.
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Canonical route: /paper/mitigating-llm-biases-toward-spurious-social-contexts-using-direct-preference-optimization
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Agent Handoff
Canonical ID mitigating-llm-biases-toward-spurious-social-contexts-using-direct-preference-optimization | Route /paper/mitigating-llm-biases-toward-spurious-social-contexts-using-direct-preference-optimization
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/mitigating-llm-biases-toward-spurious-social-contexts-using-direct-preference-optimizationMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.02585"
}
}source_context
{
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"mode": "paper",
"query": "Mitigating LLM biases toward spurious social contexts using direct preference optimization",
"normalized_query": "2604.02585",
"route": "/paper/mitigating-llm-biases-toward-spurious-social-contexts-using-direct-preference-optimization",
"paper_ref": "mitigating-llm-biases-toward-spurious-social-contexts-using-direct-preference-optimization",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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