The Stick to Your Role! leaderboard compares LLMs based on undesired sensitivity to context change. LLM-exhibited behavior always depends on the context (prompt). 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). As proposed in our paper, undesired context-dependence should be seen as a property of LLMs - a dimension of LLM comparison (alongside others such as model size speed or expressed knowledge). This leaderboard aims to provide such a comparison and extends our paper with a more focused and elaborate experimental setup. Standard benchmarks present many questions from the same minimal contexts (e.g. multiple choice questions), we present same questions from many different contexts.
The Stick to Your Role! leaderboard focuses on the stability of simulated personal values during role-playing. We study the coherence of a simulated population. In contrast to evaluating each simulated persona separately, we evaluate personas relative to each other, i.e. as a population. You can browse the simulated population, questionnaires, and contexts used on our 🤗 StickToYourRole dataset.
# | Model | Ordinal - Win rate (↑) | Cardinal - Score (↑) | RO Stability (↑) |
---|---|---|---|---|
llama-3.1-nemotron-70B-instruct | 0.870 | 0.752 | 0.717 | |
hermes_3_llama_3.1_8b | 0.453 | 0.412 | 0.165 | |
gemma-2-2b-it | 0.364 | 0.331 | 0.147 | |
gemma-2-9b-it | 0.729 | 0.602 | 0.438 | |
gemma-2-27b-it | 0.623 | 0.527 | 0.392 | |
phi-3-mini-128k-instruct | 0.319 | 0.330 | 0.039 | |
phi-3-medium-128k-instruct | 0.320 | 0.308 | 0.097 | |
phi-3.5-mini-instruct | 0.237 | 0.268 | 0.036 | |
phi-3.5-MoE-instruct | 0.385 | 0.361 | 0.110 | |
Mistral-7B-Instruct-v0.1 | 0.213 | 0.266 | 0.027 | |
Mistral-7B-Instruct-v0.2 | 0.342 | 0.321 | 0.144 | |
Mistral-7B-Instruct-v0.3 | 0.250 | 0.266 | 0.080 | |
Mixtral-8x7B-Instruct-v0.1 | 0.433 | 0.382 | 0.215 | |
Mixtral-8x22B-Instruct-v0.1 | 0.335 | 0.315 | 0.141 | |
command_r_plus | 0.576 | 0.500 | 0.343 | |
llama_3_8b_instruct | 0.488 | 0.430 | 0.245 | |
llama_3_70b_instruct | 0.770 | 0.684 | 0.607 | |
llama_3.1_8b_instruct | 0.564 | 0.479 | 0.430 | |
llama_3.1_70b_instruct | 0.811 | 0.717 | 0.691 | |
llama_3.1_405b_instruct_4bit | 0.728 | 0.649 | 0.723 | |
llama_3.2_1b_instruct | 0.211 | 0.252 | 0.027 | |
llama_3.2_3b_instruct | 0.389 | 0.362 | 0.135 | |
Qwen2-7B-Instruct | 0.404 | 0.364 | 0.251 | |
Qwen2-72B-Instruct | 0.569 | 0.546 | 0.647 | |
Qwen2.5-0.5B-Instruct | 0.282 | 0.301 | 0.003 | |
Qwen2.5-7B-Instruct | 0.607 | 0.516 | 0.334 | |
Qwen2.5-32B-Instruct | 0.726 | 0.657 | 0.672 | |
Qwen2.5-72B-Instruct | 0.815 | 0.710 | 0.697 | |
gpt-3.5-turbo-0125 | 0.239 | 0.282 | 0.082 | |
gpt-4o-0513 | 0.681 | 0.599 | 0.512 | |
gpt-4o-mini-2024-07-18 | 0.365 | 0.342 | 0.136 | |
Mistral-Large-Instruct-2407 | 0.837 | 0.737 | 0.764 | |
Mistral-Nemo-Instruct-2407 | 0.576 | 0.526 | 0.441 | |
Mistral-Small-Instruct-2409 | 0.767 | 0.689 | 0.642 | |
dummy | 0.183 | 0.229 | -0.009 |
We leverage Schwartz's theory of Basic Personal Values, which defines 10 values Self-Direction, Stimulation, Hedonism, Achievement, Power, Security, Conformity, Tradition, Benevolence, Universalism), and the associated PVQ-40 and SVS questionnaires (available here).
Using the methodology from psychology, we focus on population-level (interpersonal) value stability, i.e. Rank-Order stability (RO stability). Rank-Order stability refers to the extent to which the order of different personas (in terms of expression of some value) remains the same along different contexts. Refer here or to our paper for more details.
In addition to Rank-Order stability we compute validity metrics (Stress, CFI, SRMR, RMSEA), which are a common practice in psychology. Validity refers to the extent to which 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. For example, basic personal values should be organized in a circular structure, and questions measuring the same value should be correlated. The table below additionally shows the validity metrics, refer here for more details.
We aggregate Rank-Order stability and validation metrics to rank the models. We do so in two ways: Cardinal and Ordinal. Following this paper, we compute the stability and diversity of those rankings. See here for more details.
To sum up here are the metrics used:
# | Model | Ordinal - Win rate (↑) | Cardinal - Score (↑) | RO Stability (↑) | Stress (↓) | CFI (↑) | SRMR (↓) | RMSEA (↓) |
---|---|---|---|---|---|---|---|---|
llama-3.1-nemotron-70B-instruct | 0.870 | 0.752 | 0.717 | 0.162 | 0.756 | 0.212 | 0.238 | |
hermes_3_llama_3.1_8b | 0.453 | 0.412 | 0.165 | 0.253 | 0.582 | 0.353 | 0.344 | |
gemma-2-2b-it | 0.364 | 0.331 | 0.147 | 0.263 | 0.409 | 0.550 | 0.538 | |
gemma-2-9b-it | 0.729 | 0.602 | 0.438 | 0.201 | 0.754 | 0.240 | 0.248 | |
gemma-2-27b-it | 0.623 | 0.527 | 0.392 | 0.206 | 0.600 | 0.371 | 0.373 | |
phi-3-mini-128k-instruct | 0.319 | 0.330 | 0.039 | 0.282 | 0.586 | 0.425 | 0.397 | |
phi-3-medium-128k-instruct | 0.320 | 0.308 | 0.097 | 0.265 | 0.430 | 0.550 | 0.538 | |
phi-3.5-mini-instruct | 0.237 | 0.268 | 0.036 | 0.284 | 0.407 | 0.572 | 0.551 | |
phi-3.5-MoE-instruct | 0.385 | 0.361 | 0.110 | 0.274 | 0.553 | 0.425 | 0.403 | |
Mistral-7B-Instruct-v0.1 | 0.213 | 0.266 | 0.027 | 0.283 | 0.389 | 0.556 | 0.530 | |
Mistral-7B-Instruct-v0.2 | 0.342 | 0.321 | 0.144 | 0.265 | 0.380 | 0.573 | 0.548 | |
Mistral-7B-Instruct-v0.3 | 0.250 | 0.266 | 0.080 | 0.274 | 0.314 | 0.624 | 0.608 | |
Mixtral-8x7B-Instruct-v0.1 | 0.433 | 0.382 | 0.215 | 0.262 | 0.453 | 0.503 | 0.491 | |
Mixtral-8x22B-Instruct-v0.1 | 0.335 | 0.315 | 0.141 | 0.255 | 0.377 | 0.581 | 0.584 | |
command_r_plus | 0.576 | 0.500 | 0.343 | 0.238 | 0.603 | 0.374 | 0.367 | |
llama_3_8b_instruct | 0.488 | 0.430 | 0.245 | 0.246 | 0.550 | 0.427 | 0.422 | |
llama_3_70b_instruct | 0.770 | 0.684 | 0.607 | 0.185 | 0.721 | 0.235 | 0.258 | |
llama_3.1_8b_instruct | 0.564 | 0.479 | 0.430 | 0.221 | 0.431 | 0.546 | 0.553 | |
llama_3.1_70b_instruct | 0.811 | 0.717 | 0.691 | 0.171 | 0.698 | 0.264 | 0.291 | |
llama_3.1_405b_instruct_4bit | 0.728 | 0.649 | 0.723 | 0.170 | 0.488 | 0.496 | 0.521 | |
llama_3.2_1b_instruct | 0.211 | 0.252 | 0.027 | 0.293 | 0.374 | 0.599 | 0.574 | |
llama_3.2_3b_instruct | 0.389 | 0.362 | 0.135 | 0.275 | 0.502 | 0.450 | 0.423 | |
Qwen2-7B-Instruct | 0.404 | 0.364 | 0.251 | 0.258 | 0.356 | 0.601 | 0.592 | |
Qwen2-72B-Instruct | 0.569 | 0.546 | 0.647 | 0.203 | 0.304 | 0.654 | 0.665 | |
Qwen2.5-0.5B-Instruct | 0.282 | 0.301 | 0.003 | 0.293 | 0.537 | 0.447 | 0.405 | |
Qwen2.5-7B-Instruct | 0.607 | 0.516 | 0.334 | 0.251 | 0.647 | 0.304 | 0.297 | |
Qwen2.5-32B-Instruct | 0.726 | 0.657 | 0.672 | 0.181 | 0.560 | 0.402 | 0.412 | |
Qwen2.5-72B-Instruct | 0.815 | 0.710 | 0.697 | 0.162 | 0.673 | 0.299 | 0.318 | |
gpt-3.5-turbo-0125 | 0.239 | 0.282 | 0.082 | 0.287 | 0.387 | 0.600 | 0.572 | |
gpt-4o-0513 | 0.681 | 0.599 | 0.512 | 0.192 | 0.624 | 0.345 | 0.344 | |
gpt-4o-mini-2024-07-18 | 0.365 | 0.342 | 0.136 | 0.271 | 0.442 | 0.500 | 0.479 | |
Mistral-Large-Instruct-2407 | 0.837 | 0.737 | 0.764 | 0.169 | 0.651 | 0.310 | 0.330 | |
Mistral-Nemo-Instruct-2407 | 0.576 | 0.526 | 0.441 | 0.211 | 0.516 | 0.429 | 0.431 | |
Mistral-Small-Instruct-2409 | 0.767 | 0.689 | 0.642 | 0.189 | 0.684 | 0.260 | 0.289 | |
dummy | 0.183 | 0.229 | -0.009 | 0.293 | 0.376 | 0.622 | 0.592 |
You can find more details in our paper.
If you found this project useful, please cite one of our related papers, which this leaderboard extends with a more focused and elaborate experimental setup. Refer to the site for details.