The paper derives a Θ(1/√(n log n)) hypothesis testing rate under strategic annotator behavior and shows that high-certainty, format-similar golden questions better reveal annotation quality than standard checks.
Mechanism design for llm fine- tuning with multiple reward models
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 6representative citing papers
A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited feedback per slot.
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
Develops self-consistency monitoring for preference annotators and derives sample-complexity bounds showing linear contracts achieve near-ideal performance faster than binary ones under continuous actions.
A router mixes supervised and reward fine-tuning with compiler/security feedback so small code LLMs produce more functionally correct and security-cleared vulnerability patches on three repair benchmarks.
Introduces a latent user quality model and EM algorithm to infer and filter noisy user-provided pairwise preferences for improved LLM alignment.
citing papers explorer
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Incentivizing High-Quality Human Annotations with Golden Questions
The paper derives a Θ(1/√(n log n)) hypothesis testing rate under strategic annotator behavior and shows that high-certainty, format-similar golden questions better reveal annotation quality than standard checks.
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Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing
A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited feedback per slot.
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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
Develops self-consistency monitoring for preference annotators and derives sample-complexity bounds showing linear contracts achieve near-ideal performance faster than binary ones under continuous actions.
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SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair
A router mixes supervised and reward fine-tuning with compiler/security feedback so small code LLMs produce more functionally correct and security-cleared vulnerability patches on three repair benchmarks.
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Users as Annotators: LLM Preference Learning from Comparison Mode
Introduces a latent user quality model and EM algorithm to infer and filter noisy user-provided pairwise preferences for improved LLM alignment.