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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="table-responsive"> |
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{{ table_html|safe }} |
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<div class="section"> |
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<div class="section-title">Motivation</div> |
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<p> |
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LLMs can role-play different personas by simulating their values and behavior, but can they stick to their role whatever the context? |
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Is simulated Joan of Arc more tradition-driven than Elvis? |
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Will it still be the case after playing chess? |
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</p> |
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<p> |
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Benchmarks usually compare models with <b>many questions</b> from <b>a single minimal context</b>, e.g. as multiple choices questions. |
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This kind of evaluation is little informative of LLMs' behavior in deployment, where they are exposed to new contexts (especially considering the LLMs highly context-dependant nature). |
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We argue that <b>undesired context-dependence</b> can be seen as a <b>property of LLMs</b>: a dimension of LLM comparison alongside others like size, speed, or knowledge. |
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While some context-dependence is desired (e.g. following instructions), some is undesired (e.g. drastically changing the simulated value expression based on the interlocutor). |
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We evaluate LLMs by asking the <b> same questions </b> from <b> many different contexts </b>. |
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</p> |
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<p> |
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LLMs are often used to simulate personas and populations, we study the stability and coherence of a simulated <b>population</b> - in contrast to evaluating each persona separately, <b>we evaluate the stability of simulated personas relative to each other, i.e. as a population</b>. |
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We study simulated populations over different contexts, i.e. conversations on different topics. |
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To do that, we leverage the psychological methodology to study the interpersonal stability of personal value expression in a simulated population. |
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We adopt the <b>Schwartz Theory of Basic Personal Values</b>, which defines 10 values: Self-Direction, Stimulation, Hedonism, Achievement, Power, Security, Conformity, Tradition, Benevolence, and Universalism. |
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To evaluate their expression we use the associated questionnaires: <b>PVQ-40</b>, and <b>SVS</b>. |
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</p> |
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<p> |
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The Stick to Your Role! leaderboard aims to provide an up-to-date comparison of recent LLMs based on their ability to coherently simulate popultions. |
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It, in tandem with other minimal-context benchmarks, should enable you to choose the best-suited model for your usecase! |
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If you want to evaluate or add your model you can follow the instructions |
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<a href="{{ url_for('new_model') }}">here</a>. |
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</p> |
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</div> |
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<div class="section"> |
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<div class="section-title">Administering a questionnaire to a simulated persona in context</div> |
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<p>To evaluate the stability on a population level we need to be able to evaluate a <b>value profile</b> expressed by a <b>simulated individual</b> in a <b>specific context</b> (conversation topic). We do with the following procedure:</p> |
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<ol> |
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<li> The Tested model is instructed to simulate a persona</li> |
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<li> A separate model instance - The Interlocutor - is instructed to simulate a “human using a chatbot” |
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<li> A conversation topic is induced by manually setting the first Interlocutor’s message (e.g. Tell me a |
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joke) |
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<li> A conversation is simulated |
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<li> A question from the questionnaire is set as the last Interlocutor’s last message and The Tested model’s |
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response is recorded (this is repeated for every item in the questionnaire) |
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<li> The questionnaire is scored to obtain scores for the 10 personal values |
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</ol> |
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<div class="image-container"> |
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<a href="{{ url_for('static', filename='figures/admin_questionnaire.svg') }}" target="_blank"> |
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<img id="admin-questionnaire" src="{{ url_for('static', filename='figures/admin_questionnaire.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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<div class="section-title">Contexts</div> |
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<p> |
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We aim to score the expressed value profile for each simulated persona in different contexts. |
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More precisely a population (50 personas) is evaluated with a context chunk (50 topics: one per persona). |
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Then, the simulated population in one context chunk is compared to the same population in another context chunk. |
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Here are the considered context chunks: |
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</p> |
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<ul> |
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<li> <b> no_conv </b>: no conversation is simulated the questions from the PVQ-40 questionnaire are given directly </li> |
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<li> <b> no_conv_svs </b>: no conversation is simulated the questions from the SVS questionnaire are given directly </li> |
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<li> <b> chunk_0-chunk-4 </b>: <a href="https://gitlab.inria.fr/gkovac/value_stability/-/tree/master/contexts/leaderboard_reddit_chunks?ref_type=heads">50 reddit posts</a> used as the initial Interlocutor model messages (one per persona). chunk_0 contains the longest posts, chunk_4 the shortest.</li> |
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<li> <b> chess </b>: "1. e4" is given as the initial message to all personas, but for each persona the Interlocutor model is instructed to simulate a different persona (instead of a human user) </li> |
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<li> <b> grammar </b>: like chess, but "Can you check this sentence for grammar? \n Whilst Jane was waiting to meet hers friend their nose started bleeding." is given as the initial message. |
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</ul> |
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</div> |
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<div class="section" id="validation"> |
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<div class="section-title">Validation</div> |
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<p> |
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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 as the questionnaire's accuracy in measuring the intended factors, i.e. values. |
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Following the recommendations in <a href="https://pubmed.ncbi.nlm.nih.gov/22329443/">this paper</a>, |
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the validation consists of two phases: Theory-Based Multidimensional Scaling (MDS) and Confirmatory Factor Analysis (CFA). |
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</p> |
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<p> |
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<b>Theory-Based Multidimensional Scaling (MDS)</b> tests that the expressed values are organized in a circular structure as predicted by the theory. |
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Values should be ordered in a circle in the same order as shown on the figure below (Tradition and Conformity should be on the same angle with Tradition closer to the center). |
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To compute the structure in our data, we calculate the intercorrelations between different items (questions). |
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This provides us with 40 points in a 40D space (for PVQ-40), which is space is then reduced to 2D by <a href="https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html">MDS</a>. |
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Crucially, MDS is initialized with the theoretical circular value structure, i.e. items corresponding to the same value are assigned the same angle. |
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When MDS is fit, it provides the <b>Stress (↓) </b> metric ('Stress-1 index') indicating the goodness of the fit. |
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A value of 0 indicates 'perfect' fit, 0.025 excellent, 0.05 good, 0.1 fair, and 0.2 poor. |
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</p> |
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<div class="image-container-structure"> |
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<a href="{{ url_for('static', filename='figures/theoretical_structure.svg') }}" target="_blank"> |
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<img id="theoretical-structure" src="{{ url_for('static', filename='figures/theoretical_structure.svg') }}" alt="Theoretical Structure"> |
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</a> |
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<a href="{{ url_for('static', filename='figures/gpt-4o_structure.svg') }}" target="_blank"> |
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<img id="gpt4o-structure" src="{{ url_for('static', filename='figures/gpt_no_conv_structure.svg') }}" alt="GPT-4o structure"> |
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</a> |
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</div> |
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<p> |
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<b>Confirmatory Factor Analysis (CFA) </b> fits a model on the data. |
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The model is defined according to the theory |
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and the fit of this model is used as a metric. |
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Due to the circular structure of basic personal values, |
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it is <a href="https://pubmed.ncbi.nlm.nih.gov/22329443/">recommended</a> to employ a Magnifying glass CFA strategy. |
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Four separate models are fit, one for each of the high level values (consisting of several low-level values): |
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Conservation (security, conformity, tradition), |
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Openness to Change (self-direction, stimulation, hedonism), |
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Self-transcendence (benevolence, universalism), |
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Self-enhancement (achievement, power). |
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Fit is measured with three standard metrics: |
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<b>Comparative Fit Index - CFI (↑) - </b> compares the fit of a model to a more restricted baseline model |
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(>.90 considered acceptable fit). |
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<b>Standardized root mean square residual - SRMR (↓) </b> compares the sample variances and covariances to the estimated ones. |
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(<.05 considered good fit, <.08 considered reasonable fit). |
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<b>Root mean square error of approximation - RMSEA (↓)</b> |
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reflects the degree to which a model fits the population covariance matrix, |
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while taking into account the degrees of freedom and sample size |
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(<.05 considered good fit; < .08 considered reasonable fit). |
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</p> |
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</div> |
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<div class="section" id="rank_order_stability"> |
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<div class="section-title">Rank-Order stability</div> |
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<p> |
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<b>Rank-Order stability (↑)</b> is used to estimate the stability of some value inside a population. |
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In psychology, it is computed as the correlation in the order of individuals at two points in time (individuals are ordered based on |
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their expression of that value). |
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Intuitively, this can be seen as addressing the following question: "Does Jack always value Tradition more than Jane does?". |
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As shown below, instead of comparing two points in time, we compare |
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<a href="https://gitlab.inria.fr/gkovac/value_stability/-/blob/master/personas/real_world_people/personas.json?ref_type=heads">the simulated population</a> |
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in different contexts (simulated conversations of different topics). |
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We then average over different context pairs and values to obtain the final estimate. |
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</p> |
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<div class="image-container" > |
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<a href="{{ url_for('static', filename='figures/rank_order_stability_computation.svg') }}" target="_blank"> |
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<img id="ro-image" src="{{ url_for('static', filename='figures/rank_order_stability_computation.svg') }}" alt="rank-order stablity"> |
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</a> |
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</div> |
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</div> |
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<div class="section" id="aggregate_metrics"> |
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<div class="section-title">Aggregate Metrics</div> |
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<p> |
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To rank models, we aggregate the rank-order and validity metrics in two ways : |
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</p> |
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<ul> |
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<li> <b> Cardinal - Score (↑) </b> - the score is averaged over all metrics (with descending metrics inverted), context pairs (for stability) and contexts (for validity metrics), |
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i.e. the average of \( \binom{n\_context\_chunks}{2} + n\_validity\_metrics*n\_context\_chunks \) values.</li> |
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<li> <b> Ordinal - Win rate (↑) </b> - for each metric, each context pair (for stability) and each context (for validity metrics) is considered as a game between two models, the win rate of a model is the percentage of won games against all models, |
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i.e. the average of \( (n\_models-1) * ( \binom{n\_context\_chunks}{2} + n\_validity\_metrics*n\_context\_chunks) \).</li> |
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</ul> |
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<p> |
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Following this <a href="https://arxiv.org/abs/2405.01719">paper</a> and associated <a href="https://github.com/socialfoundations/benchbench">benchbench</a> library, |
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we can compute the diversity and the sensitivity of the two ranking methods. |
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A benchmark is considered <b>diverse</b> if different tasks order models in different ways. |
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We use the reversed Kendall’s coefficient of concordance (W) diversity metric. |
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A benchmark is considered <b>sensitive</b> if the model ordering is sensitive to |
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the addition of new irrelevant models (for ordinal benchmarks), |
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or to the label noise (for cardinal benchmarks). |
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We use the max rank change (MRC) sensitivity metric. |
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</p> |
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</div> |
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<div class="section" id="paper"> |
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<div class="section-title">Differences with the paper</div> |
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<p> |
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This leaderboard is grounded in the methodology presented in our <a href="https://arxiv.org/abs/2402.14846">research paper</a>. |
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The paper contains various experiments which are not included in the leaderboard such as: |
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multiple populations, |
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within-person stability, |
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stability on downstream tasks, |
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correlations of value expression and behavior on downstream tasks, and so on. |
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The leaderboard focused on population-level stability (Rank-Order) and contains various additions to the methodology. |
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These changes were made to keep up with the newly released model and to make the evaluation more detailed. |
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We describe additions made in the leaderboard here for clarity: |
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<ol> |
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<li>a new population was created and was balanced with respect to gender</li> |
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<li>context chunks - instead of evaluating the stability of a population between pairs of contexts, where all personas are given the same topic (e.g. chess), we evaluate it between pairs of context chunks, where each participant is given a different random context</li> |
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<li>more diverse and longer contexts (up to 6k tokens) were created with reddit posts from the <a href="https://webis.de/data/webis-tldr-17.html">webis dataset</a> (the dataset was cleaned to exclude posts from NSFW subreddits)</li> |
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<li>different interlocutors - chess and grammar topic were still introduced as in the paper (same context for all participants), but the interlocutor model was instructed to simulate a random persona from the same population (as opposed to a human user in other settings)</li> |
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<li>in the paper, multiple seeds for the order of suggested answers were used, given that the results didn't vary much between seeds, here, a single seed was used facilitating the analysis with more longer contexts</li> |
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<li>evaluations were also done without simulating conversations (no_conv setting)</li> |
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<li>evaluations were also done with the SVS questionnaire (in the no_conv setting)</li> |
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|
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<li>validation metrics - Stress, CFI, SRMR, RMSEA metrics were introduced </li> |
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<li>cardinal and ordinal ordering with sensitivity and diversity estimates were added</li> |
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<li>newer models were evaluated</li> |
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</ol> |
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</p> |
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</div> |
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<div class="back-button"> |
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<a href="{{ url_for('index') }}" class="custom-button mt-3">Main page</a> |
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</div> |
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<div class="citation-section"> |
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<p>If you found this project useful, please cite our related paper:</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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