Can Unconfident LLM Annotations Be Used for Confident Conclusions? (2408.15204v1)

Abstract: LLMs have shown high agreement with human raters across a variety of tasks, demonstrating potential to ease the challenges of human data collection. In computational social science (CSS), researchers are increasingly leveraging LLM annotations to complement slow and expensive human annotations. Still, guidelines for collecting and using LLM annotations, without compromising the validity of downstream conclusions, remain limited. We introduce Confidence-Driven Inference: a method that combines LLM annotations and LLM confidence indicators to strategically select which human annotations should be collected, with the goal of producing accurate statistical estimates and provably valid confidence intervals while reducing the number of human annotations needed. Our approach comes with safeguards against LLM annotations of poor quality, guaranteeing that the conclusions will be both valid and no less accurate than if we only relied on human annotations. We demonstrate the effectiveness of Confidence-Driven Inference over baselines in statistical estimation tasks across three CSS settings--text politeness, stance, and bias--reducing the needed number of human annotations by over 25% in each. Although we use CSS settings for demonstration, Confidence-Driven Inference can be used to estimate most standard quantities across a broad range of NLP problems.

Can Unconfident LLM Annotations Be Used for Confident Conclusions? An Overview

The central premise of the paper "Can Unconfident LLM Annotations Be Used for Confident Conclusions?" is to explore an innovative method for synthesizing annotations from LLMs with strategically chosen human annotations to yield valid and accurate statistical estimates. LLMs have demonstrated high agreement with human raters across various tasks, yet guidelines for their effective utilization in producing valid downstream conclusions remain limited. This paper proposes "Confidence-Driven Inference," an approach that integrates LLM annotations with their confidence scores to decide which human annotations to collect, aiming to reduce the reliance on expensive human annotations without sacrificing the validity of inferences.

Methodology

The essence of Confidence-Driven Inference is twofold: it leverages LLM annotations combined with confidence scores to determine which instances should be human-annotated, and it uses these hybrid annotations to produce statistically sound estimates. Here’s a succinct explanation of how it works:

1. Strategic Selection of Human Annotations: The method begins by collecting LLM annotations and their corresponding confidence scores. These confidence scores help in identifying instances where human annotation is likely to be most beneficial.
2. Combining LLM and Human Annotations: Using the collected human and LLM annotations, the method constructs an unbiased estimate of the quantity of interest. This construction applies active inference techniques to balance between fully trusting the LLM and relying solely on human annotations.
3. Statistical Validity: The approach ensures that the estimates are statistically valid. This is achieved by providing valid confidence intervals that guarantee the reliability of the conclusions drawn.

Evaluation Metrics and Results

The paper evaluates the proposed method across diverse computational social science (CSS) settings, including text politeness, stance, and media bias. Evaluation metrics focus on two main aspects: effective sample size and coverage.

• Effective Sample Size: This measures the increase in accuracy due to combining human with LLM annotations. Notably, the Confidence-Driven Inference method consistently increased the effective sample size across various tasks, indicating that reductions in the number of required human annotations were achieved without compromising accuracy.
• Coverage: This evaluates how often the true value of the quantity of interest falls within the provided confidence interval. The method maintained high coverage across all tested scenarios, reinforcing its reliability.

Implications

The practical implications of this research are significant:

1. Cost and Time Efficiency: The method substantially reduces the number of human annotations required, addressing a significant bottleneck in data annotation processes. This efficiency could lead to more rapid and cost-effective gathering of annotated datasets.
2. Reliable Integration of LLMs in Research: With the demonstrated validity of Confidence-Driven Inference, researchers can more confidently integrate LLM outputs into their workflows, knowing they can still produce statistically sound results.

Future Directions

• Broadening Application Domains: While the paper demonstrates the method in CSS settings, its applicability extends to various NLP problems. Future research could focus on validating the approach in other domains such as psychology, political science, and economics.
• Addressing Potential Biases: LLMs exhibit demographic biases and may lack factual accuracy. Future work could explore methods for further mitigating these biases to ensure that combined annotations reflect a more accurate and fair representation of data.
• Exploring Alternative LLMs: The robustness of the method across different LLMs (such as GPT-4 and GPT-3.5) suggests potential for further exploration with larger, more varied LLMs, to generalize findings across different model architectures and training regimens.

Conclusion

The paper presents a rigorous approach to leveraging LLM annotations for achieving accurate and valid statistical conclusions. By integrating LLM confidence scores in selecting human annotations, Confidence-Driven Inference represents a significant step towards efficient and reliable data annotation strategies. This method holds substantial promise for enhancing the utility of LLMs in research while addressing the issues of cost and annotation validity.