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Arxiv
1d
131
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Incentivizing Quality Text Generation via Statistical Contracts
- Large language models (LLMs) have increased the demand for machine-generated text.
- Current pay-per-token pricing schemes lead to a misalignment of incentives known as moral hazard.
- There is a strong incentive for text-generating agents to prefer a cheaper model over a cutting-edge one.
- This preference can be done internally, impacting the quality of generated text.
- To address this issue, a pay-for-performance, contract-based framework is proposed to incentivize text quality.
- The framework involves a principal-agent game where the agent generates text using costly inference.
- Contracts determine the principal's payment based on an automated quality evaluation of the text.
- Standard contract theory is insufficient when internal inference costs are unknown.
- Cost-robust contracts are introduced to deal with this uncertainty.
- Optimal cost-robust contracts are characterized through a connection to optimal composite hypothesis tests from statistics.
- Empirical evaluation of the framework involves deriving contracts for various objectives and LLM evaluation benchmarks.
- Cost-robust contracts show only a slight decrease in objective value compared to their cost-aware counterparts.
- The study offers insights into incentivizing quality text generation through contracts.
- Cost-robust contracts are found to be effective in maintaining text quality while addressing cost considerations.
- The work bridges economic principles and statistical approaches to improve text generation incentives.
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