Evaluating Adaptive Inference for African Language NLI: Challenges with Existing Benchmarks and Representation Statistics

This research investigates the effectiveness of using internal representation statistics to estimate example-level difficulty for adaptive inference in natural language processing (NLP) tasks involving African languages. The study specifically focuses on Natural Language Inference (NLI) across 15 African languages, utilizing pre-trained, off-the-shelf models.

A key finding highlights significant issues with the AfriXNLI benchmark, revealing that its English, French, and Swahili configurations largely overlap with the XNLI evaluation data. This overlap compromises their utility as clean evaluation sets for models already trained on XNLI, suggesting a need for more robust and independent benchmarks for African languages.

The research also challenges the assumption that larger models consistently perform better across all African languages, noting that a larger checkpoint showed mixed results. Furthermore, it delves into the utility of different representation statistics, finding that while some might predict computational benefits, they often fail to serve as reliable decision variables for adaptive routing, indicating that current methods don't make adaptive inference preferable to always-expensive inference for these languages.

Ultimately, the paper concludes that under the tested conditions, no evaluated signal made adaptive routing superior to consistently using expensive inference. However, an oracle system demonstrated significant potential, suggesting that more effective difficulty signals could lead to substantial accuracy gains with reduced computational cost. This underscores the ongoing need for more sophisticated methods for adaptive inference in multilingual African NLP.