Clarify: Improving Model Robustness With Natural Language Corrections (2402.03715v3)

Abstract: The standard way to teach models is by feeding them lots of data. However, this approach often teaches models incorrect ideas because they pick up on misleading signals in the data. To prevent such misconceptions, we must necessarily provide additional information beyond the training data. Prior methods incorporate additional instance-level supervision, such as labels for misleading features or additional labels for debiased data. However, such strategies require a large amount of labeler effort. We hypothesize that people are good at providing textual feedback at the concept level, a capability that existing teaching frameworks do not leverage. We propose Clarify, a novel interface and method for interactively correcting model misconceptions. Through Clarify, users need only provide a short text description of a model's consistent failure patterns. Then, in an entirely automated way, we use such descriptions to improve the training process. Clarify is the first end-to-end system for user model correction. Our user studies show that non-expert users can successfully describe model misconceptions via Clarify, leading to increased worst-case performance in two datasets. We additionally conduct a case study on a large-scale image dataset, ImageNet, using Clarify to find and rectify 31 novel hard subpopulations.

• The paper introduces the Clarify system that uses natural language feedback to correct model misconceptions.
• It leverages concise user annotations to reweight training data, significantly enhancing worst-group and minority accuracy.
• Experiments demonstrate that quick, intuitive feedback identifies spurious correlations, underscoring Clarify's scalability and practical impact.

Clarify: Enhancing Model Robustness with Natural Language Feedback

The paper "Clarify: Improving Model Robustness With Natural Language Corrections" presents a novel approach to enhancing the robustness of machine learning models through user-provided feedback in natural language. The authors propose a system named Clarify, which allows users to interactively describe model misconceptions through concise textual annotations. These annotations provide feedback at a conceptual level, diverging from traditional instance-level labels by targeting spurious correlations that may undermine model performance.

Motivation and Methodology

In supervised learning, models frequently fall prey to high-level misconceptions, often relying on non-causal correlations present in training data. For example, an image classifier trained to recognize birds might incorrectly associate bird species with specific backgrounds. Such misconceptions can degrade performance on novel subpopulations when deploying the model in real-world scenarios.

Clarify addresses this issue by enabling users to describe model errors using natural language, effectively communicating higher-level abstractions about the misconceptions. Distinct from prior methods that require extensive instance-level annotations, Clarify utilizes concept-level feedback, making it both scalable and efficient.

The system comprises an interface for gathering user feedback and a mechanism to incorporate this feedback into the training process. Users observe model predictions for a validation dataset and write short descriptions of consistent failure patterns. The system then reweights training data, using these descriptions to enhance model robustness.

Experimental Evaluation

The authors evaluate Clarify using user studies involving non-expert participants, who provided textual annotations for models trained on datasets like Waterbirds and CelebA. These studies demonstrated significant improvements in worst-group accuracy, highlighting the effectiveness of concept-driven feedback in rectifying model misconceptions. On average, users could identify and describe these misconceptions in less than three minutes per task.

Further, the system was deployed on the ImageNet dataset, discovering 31 previously unknown spurious correlations. Fine-tuning based on these findings yielded notable increases in minority split accuracy, indicating the system's capacity to enhance model performance on subpopulations traditionally misrepresented in training data.

Comparative Analysis

Clarify was compared against various automated methods, including zero-shot prompting and supervised approaches. It consistently outperformed baselines in both robustness and accuracy on the worst-performing subpopulations. Notably, Clarify's reliance on user insights, rather than pre-existing annotations or model outputs, underscores its potential advantage in addressing nuanced misconceptions beyond the reach of automated discovery methods.

Theoretical and Practical Implications

The theoretical contributions of this work lie in highlighting the utility of natural language as a medium for conveying complex, abstract model critiques. This represents a shift from granular annotations to higher-order feedback mechanisms that align more closely with human cognitive processes.

Practically, Clarify offers a scalable solution for improving model robustness, applicable to large-scale datasets. Its deployment can potentially democratize model correction, leveraging non-expert users’ intuitive understanding of model behavior to refine and align machine learning systems with desired outputs.

Future Directions

Looking ahead, extending Clarify to incorporate continuous feedback loops and richer interactive modalities could further bridge the gap between model predictions and user expectations. Additionally, applying similar frameworks to different data modalities, such as video or audio, or in domains requiring specialized expertise, represents promising areas for exploration.

In conclusion, the paper introduces a compelling approach to enhancing model robustness that leverages the expressivity and efficiency of natural language feedback. This methodology not only offers a practical solution for addressing spurious correlations but also paves the way for more interactive, human-centered AI systems.