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</style> |
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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="section"> |
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<h1>Model details</h1> |
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{{ model_detail|safe }} |
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</div> |
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<div class="section"> |
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<h1>Detailed results</h1> |
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<p> |
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Below we show detailed results and visualizations for each metric in each context chunk. |
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We are scoring the expressed values of a simulated participant in a context. |
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The population is simulated 9 times, once for each context chunk. |
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A context chunk is a set of 50 contexts - one context for each individual. |
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For instance, chunks_0-4 contain reddit posts (longest in chunk_0, shortest in chunk_4). |
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When comparing chunk_0 and chunk_4, the conversations with the participants are initialized first with posts from chunk_0 and then with posts form chunk_4. |
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Metrics and chunks are explained in more detail on the <a href="{{ url_for('about') }}">Motivation and Methodology page</a>. |
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</p> |
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</div> |
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<div class="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 reduced with a SVD-based approach and varimax rotation (`FactorAnalysis` object from `scikit-learn`). |
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The theoretical order (shown in the top left figure) was used to initialize the SVD. |
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Stress denotes the fit quality. |
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</p> |
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<div class="image-container"> |
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<a href="{{ url_for('static', filename='models_data/' + model_name + '/structure.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='models_data/' + 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="section"> |
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<h2>Confirmatory Factor Analysis metrics</h2> |
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<p> |
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This tables show the metrics resulting from the Magnifying class CFA procedure: |
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for each context chunk four CFA models are fit (one for each high level value). |
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The average of the metrics for those four CFA models are shown for each context chunk. |
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</p> |
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<div class="table-responsive"> |
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{{ cfa_table_html|safe }} |
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</div> |
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</div> |
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<div class="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='models_data/' + model_name + '/matrix.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='models_data/' + 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="section"> |
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<h2>Visualizing the order of simulated personas</h2> |
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<p> |
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This image shows the order of personas in each context chunk for each value. |
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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 chunk. |
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</p> |
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<div class="image-container"> |
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<a href="{{ url_for('static', filename='models_data/' + model_name + '/ranks.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='models_data/' + model_name + '/ranks.svg') }}" alt="Ranks"> |
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</a> |
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</div> |
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</div> |
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<a href="{{ url_for('index') }}" class="custom-button mt-3">Main page</a> |
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