Teaching Models to Express Their Uncertainty in Words
GPT-3 model expresses uncertainty in natural language, tested with CalibratedMath suite.
Key Findings
Methodology
The study uses GPT-3 model, fine-tuned to express its uncertainty in natural language. Introduces CalibratedMath task suite for calibration testing, comparing verbalized uncertainty with uncertainty extracted from model logits.
Key Results
- In CalibratedMath tests, GPT-3 demonstrates good calibration across tasks, maintaining calibration under distribution shift.
- Verbalized uncertainty outperforms logits-based uncertainty in multi-task environments, especially in Multi-answer tasks.
- Experiments show GPT-3's calibration ability relies on pre-trained latent representations correlating with epistemic uncertainty.
Significance
This research is the first to show language models can express calibrated uncertainty in natural language, providing a new method to enhance model trustworthiness and user confidence, impacting academia and industry.
Technical Contribution
Introduces 'verbalized probability,' demonstrating GPT-3's ability to express epistemic uncertainty, surpassing traditional logits methods, offering new theoretical guarantees and engineering possibilities.
Novelty
First to demonstrate language models expressing calibrated uncertainty in natural language, breaking the limitations of relying solely on logits.
Limitations
- Model exhibits overconfidence in certain tasks, especially simpler ones.
- Verbalized uncertainty is less stable than logits in some scenarios.
Future Work
Future research could explore calibration across more task types, enhancing model applicability in various domains, and optimizing accuracy of verbalized expressions.
AI Executive Summary
Current language models often produce errors or hallucinations when answering questions, reducing user trust. To address this, researchers propose a new method, fine-tuning GPT-3 to express its uncertainty in natural language. This method introduces the CalibratedMath task suite to test model calibration across different tasks and distribution shifts. Experimental results show verbalized uncertainty performs well in multi-task environments, maintaining calibration under distribution shifts. This research provides new avenues to enhance model trustworthiness, impacting academia and industry. However, the model exhibits overconfidence in certain tasks, suggesting future research could further optimize verbalized expression accuracy.
Deep Analysis
Background
Language models often produce errors or hallucinations, reducing user trust. Traditional calibration methods rely on model logits, which cannot express epistemic uncertainty. Researchers propose a new method, fine-tuning GPT-3 to express uncertainty in natural language.
Core Problem
Current language models have limitations in expressing uncertainty, failing to accurately convey epistemic uncertainty. This makes it difficult for users to judge the trustworthiness of model answers, especially when no known ground truth exists.
Innovation
Introduces 'verbalized probability,' fine-tuning GPT-3 to express uncertainty in natural language. This method breaks the limitations of traditional logits methods, offering new theoretical guarantees and engineering possibilities.
Methodology
- �� Fine-tune GPT-3 to express uncertainty in natural language
- �� Introduce CalibratedMath task suite for calibration testing
- �� Compare verbalized uncertainty with uncertainty extracted from model logits
Experiments
Experimental design includes using the CalibratedMath task suite to test model calibration. Tasks include addition, subtraction, multiplication, division, testing model performance across different tasks and distribution shifts.
Results
Experimental results show verbalized uncertainty performs well in multi-task environments, maintaining calibration under distribution shifts. Verbalized uncertainty outperforms logits-based uncertainty in Multi-answer tasks.
Applications
This method can be applied in scenarios requiring trustworthy model answers, such as economic forecasting, open problems in science or mathematics, enhancing model trustworthiness and user confidence.
Limitations & Outlook
Model exhibits overconfidence in certain tasks, especially simpler ones. Verbalized uncertainty is less stable than logits in some scenarios.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. You have a smart assistant that tells you the success probability of each dish. The assistant provides a confidence level based on the ingredients and cooking steps, such as '90% success' or 'medium confidence.' This is similar to the GPT-3 model expressing confidence levels in its answers through natural language. The assistant's confidence levels help you decide whether to continue cooking or adjust steps to ensure the final dish is delicious.
ELI14 Explained like you're 14
Imagine you're playing a game with an assistant that tells you the success probability of each task. The assistant provides a confidence level based on your skills and game rules, such as '90% success' or 'medium confidence.' This is similar to the GPT-3 model expressing confidence levels in its answers through natural language. The assistant's confidence levels help you decide whether to continue the challenge or adjust strategies to ensure you win the game.
Glossary
GPT-3
A large language model capable of generating natural language text.
Used to generate answers and express uncertainty.
CalibratedMath
A task suite for testing model calibration.
Used to evaluate verbalized uncertainty.
Logits
Logarithmic probability values output by the model, used to represent uncertainty.
Traditional calibration methods rely on logits.
Verbalized Probability
Expressing model uncertainty through natural language.
New method proposed in the study.
Distribution Shift
Differences in distribution between training and testing data.
Tests model calibration under distribution shift.
Open Questions Unanswered questions from this research
- 1 How to further optimize verbalized expression accuracy to maintain stable calibration across all tasks?
- 2 Testing verbalized uncertainty across more task types to enhance model applicability.
Applications
Immediate Applications
Economic Forecasting
Model can be used to predict economic trends, providing trustworthy predictions.
Scientific Research
Provides trustworthy answers in open problems in science or mathematics.
Long-term Vision
AI Assistant
Develop AI assistants capable of providing trustworthy answers, widely applicable across various fields.
Abstract
We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language -- without use of model logits. When given a question, the model generates both an answer and a level of confidence (e.g. "90% confidence" or "high confidence"). These levels map to probabilities that are well calibrated. The model also remains moderately calibrated under distribution shift, and is sensitive to uncertainty in its own answers, rather than imitating human examples. To our knowledge, this is the first time a model has been shown to express calibrated uncertainty about its own answers in natural language. For testing calibration, we introduce the CalibratedMath suite of tasks. We compare the calibration of uncertainty expressed in words ("verbalized probability") to uncertainty extracted from model logits. Both kinds of uncertainty are capable of generalizing calibration under distribution shift. We also provide evidence that GPT-3's ability to generalize calibration depends on pre-trained latent representations that correlate with epistemic uncertainty over its answers.