Conformal Prediction for Natural Language Processing: A Survey (2405.01976v1)

Abstract: The rapid proliferation of LLMs and NLP applications creates a crucial need for uncertainty quantification to mitigate risks such as hallucinations and to enhance decision-making reliability in critical applications. Conformal prediction is emerging as a theoretically sound and practically useful framework, combining flexibility with strong statistical guarantees. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems that stem from the absence of uncertainty quantification. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, pointing to directions for future research and open challenges.

• The paper presents a comprehensive survey of conformal prediction methods for constructing reliable prediction sets in NLP.
• It details a framework using nonconformity scores, calibration sets, and thresholding to guarantee inclusion of true outcomes with specified confidence.
• It examines practical applications such as reducing bias in medical analyses and enhancing efficiency in large language models.

Understanding Conformal Prediction in Natural Language Processing

What is Conformal Prediction?

Conformal prediction (CP) is a statistical approach that offers a robust way of measuring the uncertainty of predictions made by machine learning models. Unlike traditional methods that might provide a point estimate or a probability distribution assumption about predictions, CP offers a way to create prediction sets that likely include the true target variable. These sets come with a guaranteed probability, ensuring the inclusion of the correct answer.

Essentially, CP is about creating a safety net for predictions.

Core Concepts of Conformal Prediction

Definitions and Ingredients

• Prediction Space: CP doesn't produce point estimates but rather prediction sets.
• Conformal Scores: These are measures indicating how 'non-conforming' or unlikely a prediction is, based on past data.
• Calibration Set: A separate data partition used to adjust the conformal scores.
• Exchangeability Assumption: CP assumes that your data can be swapped around without affecting the joint probability distribution. It's a softer assumption than i.i.d (independent and identically distributed), which is commonly assumed in conventional statistical models.

The Procedure of Generating Prediction Sets

1. Calculate Conformity Scores: Determine how similar each calibration example is compared to a new test instance.
2. Set Thresholds: Based on the desired confidence level, calculate a critical value for the scores.
3. Build Prediction Sets: Include all possible outputs whose conformity scores fall below the computed threshold.

This method ensures that the real answer is included in our prediction set with our pre-specified probability.

Practical Implications and Applications

Conformal prediction's flexibility and robust theoretical foundations make it a valuable tool in various applications within NLP:

• Medical Report Analysis: CP can provide a set of possible diagnoses with confidence, critical in making high-stakes medical decisions.
• Bias Reduction: By adjusting prediction sets, CP can help mitigate bias across different subgroups within data, adhering to fairness in model predictions.
• Efficiency in LLMs: By prudently managing the computational cost of predictions without significantly sacrificing accuracy.

Navigating Drawbacks

The approach is not without its challenges. CP requires careful management of calibration data and must handle the vast output spaces characteristic of NLP applications like text generation. The assumption of data exchangeability might not always hold, particularly in cases with complex dependencies, such as sequences of text data.

Looking Ahead: Future Development in AI

CP's utility and adaptability hint at an expansive future. Here are a few directions:

1. Improving Human-AI Interaction: Leveraging prediction sets to refine interactions in systems like recommendation engines where multiple plausible suggestions are beneficial.
2. Handling Label Variability: Utilizing CP to better handle tasks with inherently subjective or diverse correct responses, such as summarization or translation.
3. Dealing with Limited or Noisy Data: Applying CP for more robust model training and evaluation, especially where labeled data is scarce or potentially unreliable.

Concluding Thoughts

Conformal prediction introduces a powerful framework for handling uncertainty in AI predictions—a crucial aspect as AI systems increasingly permeate high-stakes domains. By continuing to explore and refine CP techniques, the field can better manage the uncertainties inherent in automated decision-making processes, leading to more reliable and trustworthy AI systems.