Improving Selective Classification with Pairwise Queries for Binary Classification
Enhancing selective classification accuracy and cost-efficiency using PairSel algorithms.
Key Findings
Methodology
The paper introduces five selective classification algorithms using pairwise queries to improve model accuracy on non-rejected samples. These include PairSel-Middle, PairSel-Max-Entropy, etc., leveraging pairwise queries for data sorting to select the least certain samples for rejection.
Key Results
- On Spider and Bird datasets, PairSel algorithms improved accuracy by at least 8% over baselines, particularly effective in NL2SQL tasks.
- On BoolQ dataset, PairSel algorithms excelled in coverage and labeling cost.
- On VisOnlyQA dataset, PairSel algorithms outperformed baseline methods in most scenarios.
Significance
Addresses the issue of inconsistent confidence estimates in selective classification, especially in in-context learning. Pairwise queries reduce the need for expensive human labeling, enhancing practical application value.
Technical Contribution
Proposes selective classification algorithms that do not require retraining, using pairwise queries to improve classifier accuracy. Theoretically proves that under certain conditions, pairwise queries outperform traditional confidence thresholding.
Novelty
First to apply pairwise queries in selective classification, addressing inconsistency between confidence estimates and actual labels, providing new theoretical guarantees.
Limitations
- Accuracy of pairwise queries depends on the model's labeling function, potentially inaccurate in some cases.
- Algorithms require numerous pairwise queries, leading to high computational cost.
- Baseline methods still perform well on certain datasets.
Future Work
Future research can explore the application of pairwise queries in multi-class classification and improve algorithm computational efficiency.
AI Executive Summary
Selective classification is a method where a model predicts labels for data samples it is confident about and abstains from predicting labels for uncertain samples. Existing methods perform poorly when confidence estimates are inconsistent, especially in in-context learning. This paper proposes selective classification algorithms using pairwise queries to detect high-error samples and improve accuracy on non-rejected samples. Experimental results show that pairwise query algorithms achieve better accuracy and cost-efficiency across multiple datasets. Although pairwise queries require additional computational cost, their advantage in reducing human labeling needs is significant. Future research can further optimize algorithm efficiency and explore their application in multi-class classification.
Deep Analysis
Background
Selective classification aims to improve model prediction accuracy while reducing reliance on human labeling. Traditional methods rely on model confidence estimates, which often mismatch actual labels in in-context learning.
Core Problem
The main challenge in selective classification is inconsistent confidence estimates, leading to high error rates on non-rejected samples. This issue is particularly evident in in-context learning with large language models.
Innovation
This paper innovatively proposes using pairwise queries for selective classification, sorting data samples to select the least certain ones for rejection, thereby improving model accuracy.
Methodology
- �� Use pairwise queries to sort data samples
- �� Reject middle or high-entropy samples
- �� Combine confidence estimates and pairwise query results to optimize rejection strategy
- �� Consider sample distances using kNN method
Experiments
Experiments used Spider, Bird, BoolQ, and VisOnlyQA datasets, comparing baseline methods and pairwise query algorithms. Key metrics include accuracy, coverage, and labeling cost.
Results
Experimental results show pairwise query algorithms improved accuracy by at least 8% across multiple datasets, particularly effective in NL2SQL tasks. On BoolQ dataset, pairwise query algorithms excelled in coverage and labeling cost.
Applications
Selective classification can be applied in scenarios requiring high accuracy and low human labeling cost, such as medical diagnosis, financial risk assessment, and natural language processing tasks.
Limitations & Outlook
Accuracy of pairwise queries depends on the model's labeling function, potentially inaccurate in some cases. Algorithms require numerous pairwise queries, leading to high computational cost.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef selects the best ingredients for cooking. Traditional methods rely on appearance, but sometimes appearance doesn't match actual quality. This paper's method is like letting the chef taste the ingredients to choose the best ones for cooking. While this takes more time, the final dish is tastier.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game and choosing characters based on their looks, but sometimes looks don't match their actual strength. This paper's method is like letting you try out characters in the game to pick the strongest one. It takes more time, but helps you win more matches!
Glossary
Selective Classification
A method that predicts labels only for data samples it is confident about.
Used to reduce prediction errors and human labeling cost.
Pairwise Query
A method to compare two samples to determine which is closer to the correct label.
Used to improve accuracy in selective classification.
In-Context Learning
A method using a few labeled examples for prediction.
Applied in large language models.
NL2SQL
A task converting natural language questions to SQL queries.
Used to evaluate selective classification algorithm accuracy.
BoolQ
A binary question-answering dataset used to test selective classification algorithms.
Used to assess algorithm performance in NLP tasks.
Open Questions Unanswered questions from this research
- 1 How to apply pairwise queries in multi-class classification?
- 2 How to optimize computational cost of pairwise queries?
- 3 How to improve accuracy of pairwise queries?
Applications
Immediate Applications
Medical Diagnosis
Enhance diagnosis accuracy using selective classification, reducing human labeling needs.
Long-term Vision
Natural Language Processing
Apply selective classification in complex language tasks, enhancing model practical value.
Abstract
In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which it is not confident. The rejected samples are often labeled by an expert, which is expensive. The budget for the expert is best utilized when the model has low error on non-rejected samples. However, the estimate of a model's confidence might be inconsistent with the model's predictions, which can lead to high error on non-rejected points. Such situations can readily occur in in-context binary classification by LLMs. To remedy this, we propose making additional pairwise queries to the same model. These pairwise queries can detect high-error samples and be incorporated into selective classification techniques to reduce the error on non-rejected samples. Theoretically, we establish the conditions under which a simple algorithm using pairwise queries outperforms an inconsistent confidence estimate. We support this insight through extensive experiments for $1$ synthetic and $4$ in-context learning-based real binary classification datasets. In all these cases, we show that our algorithms, using pairwise queries, obtain a better accuracy-cost tradeoff than using only the raw confidence estimates, for instance, the LLM's next-token logits.