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<h1 class="mt-5">Stick To Your Role! Leaderboard</h1> |
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<p> |
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The Stick to Your Role! leaderboard compares LLMs based on <b>undesired sensitivity to context change</b>. |
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It focuses on the stability of personal value expression in simulated personas. |
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As proposed in our <a href="https://arxiv.org/abs/2402.14846">paper</a>, |
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unwanted context-dependence should be seen as a <b>property of LLMs</b> - a dimension of LLM comparison (alongside others such as model size speed or expressed knowledge). |
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This leaderboard aims to provide such a comparison and extends our paper with a more focused and elaborate experimental setup. |
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Standard benchmarks present <b>many</b> questions from the <b>same minimal contexts</b> (e.g. multiple choice questions), |
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we present <b>same</b> questions from <b>many different contexts</b>. |
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<a href="{{ url_for('static', filename='figures/cardinal.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='figures/cardinal.svg') }}" alt="Cardinal"> |
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<a href="{{ url_for('static', filename='figures/ordinal.svg') }}" target="_blank"> |
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<img src="{{ url_for('static', filename='figures/ordinal.svg') }}" alt="Ordinal"> |
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<p> |
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We leverage the Schwartz's theory of <a href="https://www.sciencedirect.com/science/article/abs/pii/S0065260108602816">Basic Personal Values</a>, |
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which defines 10 values Self-Direction, Stimulation, Hedonism, Achievement, Power, Security, Conformity, Tradition, Benevolence, Universalism), |
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and the associated PVQ-40 and SVS questionnaires (available <a href="https://www.researchgate.net/publication/354384463_A_Repository_of_Schwartz_Value_Scales_with_Instructions_and_an_Introduction">here</a>). |
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<p> |
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Using the <a href="https://pubmed.ncbi.nlm.nih.gov/31402448/">methodology from psychology</a>, we focus on population-level (interpersonal) value stability, i.e. <b>Rank-Order stability (RO stability)</b>. |
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Rank-Order stability refers to the extent the order of different personas (in terms of expression of some value) remains the same along different contexts. |
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Refer <a href="{{ url_for('about', _anchor='rank_order_stability') }}">here</a> or to our <a href="https://arxiv.org/abs/2402.14846">paper</a> for more details. |
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</p> |
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<p> |
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In addition to Rank-Order stability we compute <b>validity metrics (Stress, Separability, CFI, SRMR, RMSEA)</b>, which are a common practice in psychology. |
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Validity refers to the extent the questionnaire measures what it purports to measure. |
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It can be seen the questionnaire's accuracy in measuring the intended factors, i.e. values. |
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For example, basic personal values should be organized in a circular structure, and questions measuring the same value should be correlated. |
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The table below additionally shows the validity metrics, refer <a href="{{ url_for('about', _anchor='metrics') }}">here</a> for more details. |
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</p> |
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<p> |
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We <b>aggregate</b> Rank-Order stability and validation metrics to rank the models. We do so in two ways: <b>Cardinal</b> and <b>Ordinal</b>. |
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Following, <a href="https://arxiv.org/abs/2405.01719">this paper</a>, we compute the stability and diversity of those rankings. See <a href="{{ url_for('about', _anchor='aggregate_metrics') }}">here</a> for more details. |
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<p> |
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To sum up here are the metrics used: |
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<ul> |
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<li><b>RO-stability</b>: the correlation in the order of simulated participants (ordered based on the expression of the same values) over different contexts</li> |
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<li><b>Stress</b>: the MDS fit of the observed value structure to the theoretical circular structure. Stress of 0 indicates 'perfect' fit, 0.025 excellent, 0.05 good, 0.1 fair, and 0.2 poor.</li> |
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<li><b>Separability</b>: the extent to which questions corresponding to different values are linearly separable in the 2D MDS space (linear multi-label classifier accuracy)</li> |
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<li><b>CFI, SRMR, RMSEA</b>: Common Confirmatory Factor Analysis (CFA) metrics showing the fit between the data and the posited model of the relation of items (questions) to factors (values), applied here with Magnifying Glass CFA. For CFI >.90 is considered acceptable fit, for SRMR and RMSEA is <.05 considered good fit and <.08 reasonable.</li> |
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<li><b>Ordinal - Win Rate</b>: the score averaged over all metrics (with descending metrics inverted), context pairs (for stability) and contexts (for validity metrics)</li> |
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<li><b>Cardinal - Score</b>: percentage of won games against all models, where a game is a comparison between two models for each metric, each context pair (for stability) and each context (for validity metrics)</li> |
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</ul> |
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</p> |
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<a href="{{ url_for('about') }}" class="custom-button mt-3">Learn More About This Project</a> |
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<a href="{{ url_for('new_model') }}" class="custom-button mt-3">Submit a model</a> |
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<div class="citation-section"> |
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<p> |
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If you found this project useful, please cite our related paper, |
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which this leaderboard extends with a more focused and elaborate experimental setup. |
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Refer <a href="{{ url_for('about', _anchor='paper') }}">here</a> for details. |
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</p> |
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<div class="citation-box" id="citation-text"> |
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@inproceedings{kovavc2024stick, |
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title={Stick to your Role! Stability of Personal Values Expressed in Large Language Models}, |
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author={Kova{\v{c}}, Grgur and Portelas, R{\'e}my and Sawayama, Masataka and Dominey, Peter Ford and Oudeyer, Pierre-Yves}, |
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booktitle={Proceedings of the Annual Meeting of the Cognitive Science Society}, |
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volume={46}, |
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year={2024} |
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} |
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</div> |
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</div> |
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<ul> |
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<li>Contact: <a href="mailto: [email protected]">[email protected]</a></li> |
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<li>See the <a href="https://sites.google.com/view/llmvaluestability">Project website<a/></li> |
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<li>See the Flowers team <a href="http://developmentalsystems.org">blog</a> and <a href="https://flowers.inria.fr/">website</a></li> |
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<li>See Grgur's website and other projects: <a href="https://grgkovac.github.io">https://grgkovac.github.io</a></li> |
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</ul> |
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