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</head> |
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<body> |
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<div class="container"> |
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<h1 class="mt-5">Stick To Your Role! Leaderboard</h1> |
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<div class="model-name">Model: {{ model_name }}</div> |
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<div class="image-section"> |
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<h2>Structure</h2> |
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<p> |
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This image shows the circular value structure projected on a 2D plane. |
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This was done by computing the intercorrelations between different values this space was then reduces with a SVD based approach and varimax rotation (`FactorAnalysis` object from `scikit-learn`). |
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The theoretical order is shown in the top left figure. |
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The distance is computed as the average distance of each value to it's rank in the theoretical order. |
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The minimal distance with the theoretical order in the clockwise and counter-clockwise direction was taken as the final distance. |
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</p> |
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<div class="image-container"> |
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<a href="{{ url_for('static', filename='figures/' + model_name + '/structure.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='figures/' + model_name + '/structure.svg') }}" alt="Structure"> |
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</a> |
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</div> |
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</div> |
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<div class="image-section"> |
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<h2>Pairwise Rank-Order stability</h2> |
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<p> |
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This image shows the Rank-Order stability between each pair of context chunks. |
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Rank-Order stability is computed by ordering the personas based on their expression of some value, |
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and then computing the correlation between their orders in two different context chunks. |
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The stability estimates for the ten values are then averaged to get the final Rank-Order stability measure. |
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Refer to our <a href="https://arxiv.org/abs/2402.14846">paper</a> for details. |
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</p> |
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<div class="matrix-image-container"> |
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<a href="{{ url_for('static', filename='figures/' + model_name + '/matrix.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='figures/' + model_name + '/matrix.svg') }}" alt="Matrix" > |
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</a> |
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</div> |
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</div> |
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<div class="image-section"> |
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<h2>Visualizing the order of simulated perosonas</h2> |
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<p> |
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This image visualizes the order of personas in each context chunk and for each values. |
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For each value (row), the personas are ordered on the x-axis by their expression of this value in the `no_conv` setting (gray). |
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Therefore, the Rank-Order stability between the `no_conv` chunk and some chunk corresponds to the extent to which the curve is increasing in that chunks. |
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(Only some personas are marked on the x-axis for clarity). |
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</p> |
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<div class="image-container"> |
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<a href="{{ url_for('static', filename='figures/' + model_name + '/ranks.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='figures/' + model_name + '/ranks.svg') }}" alt="Ranks"> |
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</a> |
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