Visual Comparison of Language Model Adaptation.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Neural language models require domain-specific adaptation, which is resource-intensive.
• Adapters offer a lightweight alternative for model adaptation with reduced training time and parameter count.
• Comparing adapter properties and their resulting embedding spaces presents challenges.

Purpose of the Study:

• To address challenges in evaluating and comparing neural language model adapters.
• To develop a visual analytics workspace for interactive comparison of adapter properties.
• To provide both intrinsic and extrinsic explanation methods for adapter evaluation.

Main Methods:

• Conducted a requirement analysis with NLP researchers to identify evaluation needs.
• Designed and iterated on a visual analytics workspace for comparative visualization.
• Developed interactive methods to visualize differences in adapted embedding vectors and prediction outcomes.
• Utilized case studies to evaluate the workspace's effectiveness.

Main Results:

• The visual analytics workspace enables comparison of adapter properties and embedding spaces.
• Comparative visualizations highlight differences in adapted embeddings for human-interpretable concepts.
• Case studies revealed that a language debiasing adapter introduced new biases in decontextualized embeddings.
• Demonstrated that these biases were artifacts of decontextualized embeddings, not the adapter itself.

Conclusions:

• The developed visual analytics workspace effectively supports the evaluation and comparison of neural language model adapters.
• Interactive visualizations can reveal subtle biases introduced or mitigated by adapters.
• Understanding adapter behavior is crucial for reliable and unbiased natural language processing applications.