Peering inside transformer black boxes to understand how they translate low-resource languages
Transformer models power modern NLP, but they remain black boxes. For low-resource languages like Igala, understanding how models learn to translate is critical for improving performance:
The Challenge:
How do you visualize and interpret attention mechanisms in multilingual transformers to understand what linguistic features they learn—and use those insights to improve low-resource translation?
HuggingFace Transformers doesn't expose attention weights by default. Modifying the forward pass to extract them risks breaking model behavior or slowing inference to a crawl.
Used TransformerLens hooks to intercept attention computations without modifying model weights. Implemented lazy loading to only compute attention for visualized layers. Added caching to avoid redundant forward passes.
Achieved real-time attention visualization with <200ms overhead per sentence—fast enough for interactive exploration.
Raw attention weights are 12-layer × 12-head × 512-token matrices—thousands of numbers that tell you nothing. Users need to see patterns, not data dumps.
Built interactive Plotly heatmaps with hover tooltips showing token pairs and weights. Added layer/head filtering, thresholding controls, and pattern highlighting. Annotated common patterns (diagonal = positional, vertical = broadcast attention).
Researchers without ML backgrounds can now identify syntactic vs semantic heads visually in under 5 minutes.
It's easy to see patterns that aren't really there. How do you prove that 'Head 3-5 learns word alignment' isn't just cherry-picked examples?
Tested hypotheses across 100+ diverse sentence pairs. Compared attention patterns between correct and incorrect translations. Used attention rollout to trace information flow. Documented counterexamples where patterns break down.
Findings now backed by systematic analysis, not anecdotes—increased research credibility for future work.
Attention visualization shows where models look, not why they make decisions. Here's what's missing from this analysis:
High attention weights don't prove causality. A head attending strongly to a word doesn't mean it's responsible for the translation. Need ablation studies (zero out heads, measure impact) to confirm.
What this means: I'm showing correlations ("Head 3-5 attends to aligned words"), not mechanisms ("Head 3-5 causes word alignment"). Take labels as hypotheses.
This tool only visualizes attention layers. Ignores feed-forward networks (FFNs), which process 50%+ of computation. FFNs do critical reasoning that attention analysis misses completely.
What's missing: No MLP neuron analysis, no residual stream tracking. You're seeing half the picture.
Analysis specific to mBERT architecture (bidirectional encoder). Doesn't generalize to GPT (decoder-only), T5 (encoder-decoder), or modern models like Llama. Attention patterns differ drastically across architectures.
Don't assume: Findings about "syntactic heads" in mBERT apply to other models. Each architecture needs separate analysis.
Purely observational. Can't edit activations, can't ablate circuits, can't do path patching. This is correlation analysis, not causal tracing.
To prove causality: Would need tools like TransformerLens's path patching or activation steering. This tool doesn't support that.
Analyzed 100+ sentence pairs, but Igala has thousands of grammatical structures. Patterns may not generalize to untested phenomena (rare tenses, complex embeddings, dialectal variations).
Exploratory only: This is hypothesis generation, not exhaustive analysis. Use findings to guide deeper investigation.
Labeling attention heads as "syntactic" vs "semantic" requires human judgment. No automated classifier. Different researchers might interpret same heatmap differently.
Reproducibility concern: My interpretations reflect my intuitions about Igala grammar. Native speakers might see different patterns.
Epistemic Humility
Transformer interpretability is an active research area. Current tools capture correlations, not necessarily mechanisms. I'm presenting working hypotheses (e.g., "Head 3-5 learns word alignment"), not proven facts. Validate with ablation studies before making strong claims.
For rigorous mechanistic interpretability: Use tools like Anthropic's Transformer Circuits Thread, OpenAI's Microscope, or NeelNanda's TransformerLens. This demo is for exploration and education, not publication-grade analysis.
Interact with live attention visualizations and discover how transformers learn to translate.