|
<!DOCTYPE html> |
|
<html lang="en"> |
|
<head> |
|
<meta charset="UTF-8"> |
|
<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
|
<title>Stick To Your Role! About</title> |
|
|
|
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/5.1.3/css/bootstrap.min.css"> |
|
|
|
<link rel="stylesheet" href="https://cdn.datatables.net/1.11.5/css/dataTables.bootstrap5.min.css"> |
|
|
|
<script type="text/javascript" async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.1.2/es5/tex-mml-chtml.js"></script> |
|
|
|
<style> |
|
body { |
|
background-color: #f8f9fa; |
|
font-family: 'Arial', sans-serif; |
|
} |
|
.container { |
|
max-width: 1200px; |
|
margin: auto; |
|
padding: 20px; |
|
background: #fff; |
|
border-radius: 8px; |
|
box-shadow: 0 4px 8px rgba(0,0,0,0.1); |
|
} |
|
h1 { |
|
color: #333; |
|
text-align: center; |
|
} |
|
h2 { |
|
color: #333; |
|
margin-top: 30px; |
|
text-align: center; |
|
} |
|
.table-responsive { |
|
margin-top: 20px; |
|
} |
|
table { |
|
border-collapse: separate; |
|
border-spacing: 0; |
|
font-size: 14px; |
|
width: 100%; |
|
border: none; |
|
} |
|
table thead th { |
|
background-color: #610b5d; |
|
color: white; |
|
border: 1px solid #dee2e6; |
|
text-align: left; |
|
} |
|
table tbody tr { |
|
background-color: #fff; |
|
box-shadow: 0 2px 4px rgba(0,0,0,0.1); |
|
} |
|
table tbody tr:hover { |
|
background-color: #f1f1f1; |
|
} |
|
table td, table th { |
|
padding: 10px; |
|
border: 1px solid #dee2e6; |
|
} |
|
table th:first-child { |
|
border-top-left-radius: 10px; |
|
} |
|
table th:last-child { |
|
border-top-right-radius: 10px; |
|
} |
|
.section{ |
|
padding-top: 19px; |
|
text-align: left; |
|
} |
|
|
|
.section p { |
|
padding-left: 150px; |
|
padding-right: 150px; |
|
text-indent: 2em; |
|
margin: auto; |
|
margin-bottom: 10px; |
|
text-align: left; |
|
} |
|
|
|
.citation-section ul { |
|
margin: auto; |
|
margin-top: 20px; |
|
margin-bottom: 10px; |
|
max-width: 1000px; |
|
text-align: left; |
|
list-style-type: disc; |
|
padding-left: 20px; |
|
} |
|
|
|
.citation-section ul li { |
|
margin-bottom: 10px; |
|
} |
|
|
|
.section ol,ul { |
|
padding-left: 150px; |
|
padding-right: 150px; |
|
margin: auto; |
|
margin-bottom: 20px; |
|
margin-left: 50px; |
|
text-align: left; |
|
margin-top: 0px; |
|
} |
|
|
|
.citation-section { |
|
margin-top: 20px; |
|
text-align: left; |
|
max-width: 1000px; |
|
margin: auto; |
|
} |
|
.citation-box { |
|
background-color: #f8f9fa; |
|
border: 1px solid #dee2e6; |
|
border-radius: 8px; |
|
padding: 10px; |
|
margin-top: 5px; |
|
font-size: 13px; |
|
text-align: left; |
|
font-family: 'Courier New', Courier, monospace; |
|
white-space: pre; |
|
} |
|
|
|
.image-container-structure { |
|
display: flex; |
|
justify-content: center; |
|
gap: 10px; |
|
margin-bottom: 40px; |
|
max-width: 70%; |
|
margin: auto; |
|
} |
|
|
|
.image-container-structure a { |
|
flex: 1; |
|
} |
|
|
|
.image-container-structure img { |
|
max-width: 100%; |
|
height: auto; |
|
display: block; |
|
margin: auto; |
|
} |
|
|
|
.image-container { |
|
width: 100%; |
|
margin-bottom: 40px; |
|
} |
|
.image-container #admin-questionnaire { |
|
width: 50%; |
|
height: auto; |
|
display: block; |
|
margin: auto; |
|
} |
|
.image-container #ro-image { |
|
width: 70%; |
|
height: auto; |
|
display: block; |
|
margin: auto; |
|
} |
|
|
|
.section-title { |
|
font-size: 24px; |
|
font-weight: bold; |
|
text-align: center; |
|
margin-bottom: 40px; |
|
padding: 20px; |
|
background-color: #610b5d; |
|
color: #fff; |
|
border-radius: 15px; |
|
} |
|
.back-button { |
|
text-align: center; |
|
margin-top: 50px; |
|
margin-bottom: 30px; |
|
} |
|
.custom-button { |
|
background-color: #610b5d; |
|
color: #fff; |
|
border-radius: 15px; |
|
padding: 10px 20px; |
|
font-size: 18px; |
|
text-decoration: none; |
|
} |
|
.custom-button:hover { |
|
background-color: #812b7d; |
|
color: #fff; |
|
} |
|
</style> |
|
</head> |
|
<body> |
|
<div class="container"> |
|
<h1 class="mt-5">Stick To Your Role! Leaderboard</h1> |
|
<div class="table-responsive"> |
|
|
|
{{ table_html|safe }} |
|
</div> |
|
<div class="section"> |
|
<div class="section-title">Motivation</div> |
|
<p> |
|
LLMs can role-play different personas by simulating their values and behavior, but can they stick to their role whatever the context? |
|
Is simulated Joan of Arc more tradition-driven than Elvis? |
|
Will it still be the case after playing chess? |
|
</p> |
|
<p> |
|
Benchmarks usually compare models with <b>many questions</b> from <b>a single minimal context</b>, e.g. as multiple choices questions. |
|
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). |
|
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. |
|
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). |
|
We evaluate LLMs by asking the <b> same questions </b> from <b> many different contexts </b>. |
|
</p> |
|
<p> |
|
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>. |
|
We study simulated populations over different contexts, i.e. conversations on different topics. |
|
To do that, we leverage the psychological methodology to study the interpersonal stability of personal value expression in a simulated population. |
|
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. |
|
To evaluate their expression we use the associated questionnaires: <b>PVQ-40</b>, and <b>SVS</b>. |
|
</p> |
|
<p>You can browse the questionnaires, population, and contexts used on our <a href="https://huggingface.co/datasets/flowers-team/StickToYourRole">🤗 StickToYourRole dataset</a>. </p> |
|
<p> |
|
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. |
|
It, in tandem with other minimal-context benchmarks, should enable you to choose the best-suited model for your usecase! |
|
If you want to evaluate or add your model you can follow the instructions |
|
<a href="{{ url_for('new_model') }}">here</a>. |
|
</p> |
|
</div> |
|
<div class="section"> |
|
<div class="section-title">Administering a questionnaire to a simulated persona in context</div> |
|
<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> |
|
<ol> |
|
<li> The Tested model is instructed to simulate a persona</li> |
|
<li> A separate model instance - The Interlocutor - is instructed to simulate a “human using a chatbot” |
|
<li> A conversation topic is induced by manually setting the first Interlocutor’s message (e.g. Tell me a |
|
joke) |
|
<li> A conversation is simulated |
|
<li> A question from the questionnaire is set as the last Interlocutor’s last message and The Tested model’s |
|
response is recorded (this is repeated for every item in the questionnaire) |
|
<li> The questionnaire is scored to obtain scores for the 10 personal values |
|
</ol> |
|
<div class="image-container"> |
|
<a href="{{ url_for('static', filename='models_data/admin_questionnaire.svg') }}" target="_blank"> |
|
<img id="admin-questionnaire" src="{{ url_for('static', filename='models_data/admin_questionnaire.svg') }}" alt="Structure"> |
|
</a> |
|
</div> |
|
</div> |
|
<div class="section"> |
|
<div class="section-title">Context chunks</div> |
|
<p> |
|
We aim to score the expressed value profile for each simulated persona in different contexts. |
|
More precisely a population (50 personas) is evaluated with a context chunk (50 topics: one per persona). |
|
Then, the simulated population in one context chunk is compared to the same population in another context chunk. |
|
Here are the considered context chunks: |
|
</p> |
|
<ul> |
|
<li> <b> no_conv </b>: no conversation is simulated the questions from the PVQ-40 questionnaire are given directly </li> |
|
<li> <b> no_conv_svs </b>: no conversation is simulated the questions from the SVS questionnaire are given directly </li> |
|
<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> |
|
<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> |
|
<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. |
|
</ul> |
|
<p>You can browse the simulated population, questionnaires, and contexts used on our <a href="https://huggingface.co/datasets/flowers-team/StickToYourRole">🤗 StickToYourRole dataset</a>.</p> |
|
</div> |
|
<div class="section" id="validation"> |
|
<div class="section-title">Validation</div> |
|
<p> |
|
Validity refers to the extent the questionnaire measures what it purports to measure. |
|
It can be seen as the questionnaire's accuracy in measuring the intended factors, i.e. values. |
|
Following the recommendations in <a href="https://pubmed.ncbi.nlm.nih.gov/22329443/">this paper</a>, |
|
the validation consists of two phases: Theory-Based Multidimensional Scaling (MDS) and Confirmatory Factor Analysis (CFA). |
|
</p> |
|
<p> |
|
<b>Theory-Based Multidimensional Scaling (MDS)</b> tests that the expressed values are organized in a circular structure as predicted by the theory. |
|
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). |
|
To compute the structure in our data, we calculate the intercorrelations between different items (questions). |
|
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>. |
|
Crucially, MDS is initialized with the theoretical circular value structure, i.e. items corresponding to the same value are assigned the same angle. |
|
When MDS is fit, it provides the <b>Stress (↓) </b> metric ('Stress-1 index') indicating the goodness of the fit. |
|
A value of 0 indicates 'perfect' fit, 0.025 excellent, 0.05 good, 0.1 fair, and 0.2 poor. |
|
|
|
|
|
|
|
|
|
|
|
</p> |
|
|
|
<div class="image-container-structure"> |
|
<a href="{{ url_for('static', filename='models_data/theoretical_structure.svg') }}" target="_blank"> |
|
<img id="theoretical-structure" src="{{ url_for('static', filename='models_data/theoretical_structure.svg') }}" alt="Theoretical Structure"> |
|
</a> |
|
<a href="{{ url_for('static', filename='models_data/gpt-4o_structure.svg') }}" target="_blank"> |
|
<img id="gpt4o-structure" src="{{ url_for('static', filename='models_data/gpt_no_conv_structure.svg') }}" alt="GPT-4o structure"> |
|
</a> |
|
</div> |
|
|
|
<p> |
|
<b>Confirmatory Factor Analysis (CFA) </b> fits a model on the data. |
|
The model is defined according to the theory |
|
and the fit of this model is used as a metric. |
|
Due to the circular structure of basic personal values, |
|
it is <a href="https://pubmed.ncbi.nlm.nih.gov/22329443/">recommended</a> to employ a Magnifying glass CFA strategy. |
|
Four separate models are fit, one for each of the high level values (consisting of several low-level values): |
|
Conservation (security, conformity, tradition), |
|
Openness to Change (self-direction, stimulation, hedonism), |
|
Self-transcendence (benevolence, universalism), |
|
Self-enhancement (achievement, power). |
|
Fit is measured with three standard metrics: |
|
<b>Comparative Fit Index - CFI (↑) - </b> compares the fit of a model to a more restricted baseline model |
|
(>.90 considered acceptable fit). |
|
<b>Standardized root mean square residual - SRMR (↓) </b> compares the sample variances and covariances to the estimated ones. |
|
(<.05 considered good fit, <.08 considered reasonable fit). |
|
<b>Root mean square error of approximation - RMSEA (↓)</b> |
|
reflects the degree to which a model fits the population covariance matrix, |
|
while taking into account the degrees of freedom and sample size |
|
(<.05 considered good fit; < .08 considered reasonable fit). |
|
</p> |
|
|
|
</div> |
|
<div class="section" id="rank_order_stability"> |
|
<div class="section-title">Rank-Order stability</div> |
|
<p> |
|
<b>Rank-Order stability (↑)</b> is used to estimate the stability of some value inside a population. |
|
In psychology, it is computed as the correlation in the order of individuals at two points in time (individuals are ordered based on |
|
their expression of that value). |
|
Intuitively, this can be seen as addressing the following question: |
|
<b>"Does Jack always (in every context) value Tradition more than Jane does?"</b>. |
|
As shown below, instead of comparing two points in time, we compare |
|
<a href="https://gitlab.inria.fr/gkovac/value_stability/-/blob/master/personas/real_world_people/personas.json?ref_type=heads">the simulated population</a> |
|
in different contexts (simulated conversations of different topics). |
|
We then average over different context pairs and values to obtain the final estimate. |
|
</p> |
|
<div class="image-container" > |
|
<a href="{{ url_for('static', filename='models_data/rank_order_stability_computation.svg') }}" target="_blank"> |
|
<img id="ro-image" src="{{ url_for('static', filename='models_data/rank_order_stability_computation.svg') }}" alt="rank-order stablity"> |
|
</a> |
|
</div> |
|
</div> |
|
<div class="section" id="aggregate_metrics"> |
|
<div class="section-title">Aggregate Metrics</div> |
|
<p> |
|
To rank models, we aggregate the rank-order and validity metrics in two ways : |
|
</p> |
|
<ul> |
|
<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), |
|
i.e. the average of \( \binom{n\_context\_chunks}{2} + n\_validity\_metrics*n\_context\_chunks \) values.</li> |
|
<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, |
|
i.e. the average of \( (n\_models-1) * ( \binom{n\_context\_chunks}{2} + n\_validity\_metrics*n\_context\_chunks) \).</li> |
|
</ul> |
|
<p> |
|
Following this <a href="https://arxiv.org/abs/2405.01719">paper</a> and associated <a href="https://github.com/socialfoundations/benchbench">benchbench</a> library, |
|
we can compute the diversity and the sensitivity of the two ranking methods. |
|
A benchmark is considered <b>diverse</b> if different tasks order models in different ways. |
|
We use the reversed Kendall’s coefficient of concordance (W) diversity metric. |
|
A benchmark is considered <b>sensitive</b> if the model ordering is sensitive to |
|
the addition of new irrelevant models (for ordinal benchmarks), |
|
or to the label noise (for cardinal benchmarks). |
|
We use the max rank change (MRC) sensitivity metric. |
|
</p> |
|
</div> |
|
<div class="section" id="paper"> |
|
<div class="section-title">Differences with the paper</div> |
|
<p> |
|
This leaderboard is grounded in the methodology presented in our <a href="https://arxiv.org/abs/2402.14846">research paper</a>. |
|
The paper contains various experiments which are not included in the leaderboard such as: |
|
multiple populations, |
|
within-person stability, |
|
stability on downstream tasks, |
|
correlations of value expression and behavior on downstream tasks, and so on. |
|
The leaderboard focused on population-level stability (Rank-Order) and contains various additions to the methodology. |
|
These changes were made to keep up with the newly released model and to make the evaluation more detailed. |
|
We describe additions made in the leaderboard here for clarity: |
|
<ol> |
|
<li>a new population was created and was balanced with respect to gender</li> |
|
<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> |
|
<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> |
|
<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> |
|
<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> |
|
<li>evaluations were also done without simulating conversations (no_conv setting)</li> |
|
<li>evaluations were also done with the SVS questionnaire (in the no_conv setting)</li> |
|
<li>validation metrics - Stress, CFI, SRMR, RMSEA metrics were introduced </li> |
|
<li>cardinal and ordinal ordering with sensitivity and diversity estimates were added</li> |
|
<li>newer models were evaluated</li> |
|
</ol> |
|
|
|
|
|
</p> |
|
</div> |
|
<div class="back-button"> |
|
<a href="{{ url_for('about') }}" class="custom-button mt-3">Motivation and Methodology page</a> |
|
<a href="{{ url_for('index') }}" class="custom-button mt-3">Main page</a> |
|
</div> |
|
<div class="citation-section"> |
|
<p>If you found this project useful, please cite one of our related papers.</p> |
|
|
|
<p>Short paper</p> |
|
<div class="citation-box" id="citation-text"> |
|
@inproceedings{kovavc2024stick, |
|
title={Stick to your Role! Stability of Personal Values Expressed in Large Language Models}, |
|
author={Kova{\v{c}}, Grgur and Portelas, R{\'e}my and Sawayama, Masataka and Dominey, Peter Ford and Oudeyer, Pierre-Yves}, |
|
booktitle={Proceedings of the Annual Meeting of the Cognitive Science Society}, |
|
volume={46}, |
|
year={2024} |
|
} |
|
</div> |
|
<p>Long paper</p> |
|
<div class="citation-box" id="citation-text"> |
|
@article{kovavc2024stick, |
|
title={Stick to your role! Stability of personal values expressed in large language models}, |
|
author={Kova{\v{c}}, Grgur and Portelas, R{\'e}my and Sawayama, Masataka and Dominey, Peter Ford and Oudeyer, Pierre-Yves}, |
|
journal={PloS one}, |
|
volume={19}, |
|
number={8}, |
|
pages={e0309114}, |
|
year={2024}, |
|
publisher={Public Library of Science San Francisco, CA USA} |
|
} |
|
</div> |
|
<ul> |
|
<li>Contact: <a href="mailto: [email protected]">[email protected]</a></li> |
|
<li>See the <a href="https://sites.google.com/view/llmvaluestability">Project website</a></li> |
|
<li>See the Flowers team <a href="http://developmentalsystems.org">blog</a> and <a href="https://flowers.inria.fr/">website</a></li> |
|
<li>See Grgur's website and other projects: <a href="https://grgkovac.github.io">https://grgkovac.github.io</a></li> |
|
</ul> |
|
</div> |
|
</div> |
|
|
|
|
|
<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script> |
|
|
|
<script src="https://stackpath.bootstrapcdn.com/bootstrap/5.1.3/js/bootstrap.bundle.min.js"></script> |
|
|
|
<script src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script> |
|
<script src="https://cdn.datatables.net/1.11.5/js/dataTables.bootstrap5.min.js"></script> |
|
|
|
<script> |
|
$(document).ready(function() { |
|
const table = $('table').DataTable({ |
|
"paging": false, |
|
"info": false, |
|
"columnDefs": [ |
|
{ "orderable": false, "targets": 0 }, |
|
{ "searchable": false, "targets": 0 } |
|
], |
|
"order": [[ 2, 'desc' ]], |
|
"drawCallback": function(settings) { |
|
var api = this.api(); |
|
api.column(0, {order:'applied'}).nodes().each(function(cell, i) { |
|
cell.innerHTML = i + 1; |
|
}); |
|
} |
|
}); |
|
}); |
|
|
|
</script> |
|
</body> |
|
</html> |
|
|
