A better Figure 2: the threshold the bar chart hides
The paper's key soft-mapping result is a curve with a cliff. Drawn as bars, the cliff disappears.
The original
Figure 2 mixes two kinds of things in one bar chart: conditions (baseline, same data, same initialization) and points on a dial (soft-mapping at 20% and 99.9%, where a fraction p of each token pairâs embedding is shared). The textâs main claim about this experiment is a sharp threshold: âAt 20% tying, generalization remains weak with CLeq = 8.6%; at 50% tying it jumps to 82.7%â. But the 50% point, the one that shows the jump, is not in the figure.
The full sweep is in the appendix (the soft-mapping table), and it covers two settings: the clone languages, and EnglishâWWT-Arabic. Plotted together, they tell a better story.
The redesign
Each line is normalized by its own value at full sharing (p = 100%), so both fit on one axis: âhow much of the achievable transfer do you get when sharing a fraction p of the embedding?â Hover a point for the raw CLeq.
{
"type": "line",
"data": {
"labels": ["0%", "20%", "50%", "90%", "99%", "99.9%", "100%"],
"datasets": [
{
"label": "EnglishââEnglishâ (clone languages)",
"data": [-2.6, 8.6, 82.7, 95.8, 99.1, 99.0, 100],
"borderColor": "#2f6fde",
"backgroundColor": "#2f6fde",
"borderWidth": 3,
"pointRadius": 5,
"tension": 0
},
{
"label": "EnglishâWWT-Arabic (natural pair)",
"data": [null, 7.9, 13.5, 76.2, 83.3, 81.0, 100],
"borderColor": "#e8590c",
"backgroundColor": "#e8590c",
"borderWidth": 3,
"pointRadius": 5,
"tension": 0
}
]
},
"options": {
"responsive": true,
"plugins": {
"title": { "display": true, "text": "Share of the achievable transfer vs. fraction of embedding shared" },
"legend": { "position": "bottom" }
},
"scales": {
"x": { "title": { "display": true, "text": "Fraction p of each token pair's embedding that is tied (not to scale)" } },
"y": { "min": -10, "max": 105, "title": { "display": true, "text": "% of CLeq at full sharing" } }
}
}
}
| p (tied) | 0% | 20% | 50% | 90% | 99% | 99.9% | 100% |
|---|---|---|---|---|---|---|---|
| EnââEnâ, raw CLeq | â2.6% | 8.6% | 82.7% | 95.8% | 99.1% | 99.0% | 100% |
| EnâWWT-Ar, raw CLeq | â | 1.0% | 1.7% | 9.6% | 10.5% | 10.2% | 12.6% |
What the new figure shows that the old one didnât
- The cliff is real, and itâs in a different place for the two settings. Clone languages jump between 20% and 50% sharing. The natural pair is still at 13% of its ceiling at 50%, and only jumps between 50% and 90%. Natural languages need much more sharing before knowledge flows.
- Reserving a few language-specific dimensions is cheap. At p = 90% (96 free dimensions out of 960) the natural pair keeps 76% of the benefit, which supports the paperâs âsoft mappingâ proposal for restoring language identity.
- It invites the right next question: where exactly between 50% and 90% is the WWT cliff, and why is it later?
Design decisions
- A line, not bars, because p is a dial, not a set of categories.
- Normalize each series so a 12.6%-max curve and a 100%-max curve can share an axis. The raw numbers stay in the table and tooltips.
- Conditions that arenât on the dial (same data, same initialization) belong in a separate, small panel or table, not in the same bar list.
- Honest axis: the x-axis is evenly spaced but the values are not, so it says ânot to scaleâ. Plotting the number of free dimensions on a log axis would be a scale-true alternative; try it!
Data: the appendix soft-mapping table and Figure 2 of (Gaber et al., 2026).
References
- Why Pretraining Fails to Share Cross-Lingual Knowledge2026
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