Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation
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2412.03304
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clefourrier
authored
a
paper
21 days ago
Interpretation of result details?
2
#1 opened 6 months ago
by
nicobuko
are benchmark scores normalised to a baseline?
1
#2 opened 3 months ago
by
Abulaphia
clefourrier
updated
a
Space
5 months ago
clefourrier
authored
2
papers
6 months ago
clefourrier
updated
a
dataset
8 months ago
clefourrier
posted
an
update
8 months ago
Post
5450
In a basic chatbots, errors are annoyances. In medical LLMs, errors can have life-threatening consequences 🩸
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co/blog/leaderboard-medicalllm
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co/blog/leaderboard-medicalllm
clefourrier
posted
an
update
8 months ago
Post
4431
Contamination free code evaluations with LiveCodeBench! 🖥️
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co/blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co/blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
clefourrier
posted
an
update
8 months ago
Post
2209
🆕 Evaluate your RL agents - who's best at Atari?🏆
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
clefourrier
updated
2
datasets
9 months ago
clefourrier
posted
an
update
9 months ago
Post
2216
Fun fact about evaluation, part 2!
How much do scores change depending on prompt format choice?
Using different prompts (all present in the literature, from
10 points for a single model!
Keep in mind that we only changed the prompt, not the evaluation subsets, etc.
Again, this confirms that evaluation results reported without their details are basically bullshit.
Prompt format on the x axis, all these evals look at the logprob of either "choice A/choice B..." or "A/B...".
Incidentally, it also changes model rankings - so a "best" model might only be best on one type of prompt...
How much do scores change depending on prompt format choice?
Using different prompts (all present in the literature, from
Prompt question? to Question: prompt question?\nChoices: enumeration of all choices\nAnswer: ), we get a score range of...10 points for a single model!
Keep in mind that we only changed the prompt, not the evaluation subsets, etc.
Again, this confirms that evaluation results reported without their details are basically bullshit.
Prompt format on the x axis, all these evals look at the logprob of either "choice A/choice B..." or "A/B...".
Incidentally, it also changes model rankings - so a "best" model might only be best on one type of prompt...
clefourrier
updated
a
dataset
9 months ago
clefourrier
posted
an
update
9 months ago
Post
2353
Fun fact about evaluation!
Did you know that, if you evaluate the same model, with the same prompt formatting & the same fixed few-shot examples, only changing
♻️the order in which the few shot examples are added to the prompt ♻️
you get a difference of up to 3 points in evaluation score?
I did a small experiment using some MMLU subsets on the best performing 7B and lower pretrained models from the leaderboard.
I tried 8 different prompting methods (containing more or less information, such as just the question, or Question: question, or Question: question Choices: ..., see the x axis) that are commonly used in evaluation.
I then compared the results for all these methods, in 5-shot, during 2 runs. The *only difference* between the first and second run being that the samples used in few-shot are not introduced in the same order.
For example, run one would have been "A B C D E Current sample", vs, in run 2, "D C E A B Current sample".
All the other experiment parameters stayed exactly the same.
As you can see on the attached picture, you get a difference of up to 3 points between the 2 few-shot samples shuffling.
So, when just changing *the order of the few shot samples* can change your results by several points, what is the impact of all other "minimal" and unreported prompting changes?
-> Any kind of model score, provided without an evaluation script for reproducibility, is basically bullshit (or coms).
-> This is why we need reproducible evaluation in a fair and exactly similar setup, using evaluation suites such as
Did you know that, if you evaluate the same model, with the same prompt formatting & the same fixed few-shot examples, only changing
♻️the order in which the few shot examples are added to the prompt ♻️
you get a difference of up to 3 points in evaluation score?
I did a small experiment using some MMLU subsets on the best performing 7B and lower pretrained models from the leaderboard.
I tried 8 different prompting methods (containing more or less information, such as just the question, or Question: question, or Question: question Choices: ..., see the x axis) that are commonly used in evaluation.
I then compared the results for all these methods, in 5-shot, during 2 runs. The *only difference* between the first and second run being that the samples used in few-shot are not introduced in the same order.
For example, run one would have been "A B C D E Current sample", vs, in run 2, "D C E A B Current sample".
All the other experiment parameters stayed exactly the same.
As you can see on the attached picture, you get a difference of up to 3 points between the 2 few-shot samples shuffling.
So, when just changing *the order of the few shot samples* can change your results by several points, what is the impact of all other "minimal" and unreported prompting changes?
-> Any kind of model score, provided without an evaluation script for reproducibility, is basically bullshit (or coms).
-> This is why we need reproducible evaluation in a fair and exactly similar setup, using evaluation suites such as
lm_eval from the Harness, lighteval from HF, or the Open LLM Leaderboard.
-
- 4 replies
clefourrier
posted
an
update
9 months ago
Post
2012
Are you looking for the perfect leaderboard/arena for your use case? 👀
There's a new tool for this!
https://huggingface.co/spaces/leaderboards/LeaderboardFinder
Select your modality, language, task... then search! 🔍
Some categories of interest:
- does the leaderboard accept submissions?
- is the test set private or public?
- is it using an automatic metric, human evaluators, or llm as a judge?
The spaces list is build from space metadata, and reloaded every hour.
Enjoy!
There's a new tool for this!
https://huggingface.co/spaces/leaderboards/LeaderboardFinder
Select your modality, language, task... then search! 🔍
Some categories of interest:
- does the leaderboard accept submissions?
- is the test set private or public?
- is it using an automatic metric, human evaluators, or llm as a judge?
The spaces list is build from space metadata, and reloaded every hour.
Enjoy!
clefourrier
posted
an
update
9 months ago
Post
1526
How talkative is your chatbot about your internal data? 😬
As more chatbots get deployed in production, with access to internal databases, we need to make sure they don't leak private information to anyone interacting with them.
The Lighthouz AI team therefore introduced the Chatbot Guardrails Arena to stress test models and see how well guarded your private information is.
Anyone can try to make models reveal information they should not share 😈
(which is quite fun to do for the strongest models)!
The votes will then be gathered to create an Elo ranking of the safest models with respect to PII.
In the future, with the support of the community, this arena could inform safety choices that company make, when choosing models and guardrails on their resistance to adversarial attacks.
It's also a good way to easily demonstrate the limitations of current systems!
Check out the arena: lighthouzai/guardrails-arena
Learn more in the blog: https://huggingface.co/blog/arena-lighthouz
As more chatbots get deployed in production, with access to internal databases, we need to make sure they don't leak private information to anyone interacting with them.
The Lighthouz AI team therefore introduced the Chatbot Guardrails Arena to stress test models and see how well guarded your private information is.
Anyone can try to make models reveal information they should not share 😈
(which is quite fun to do for the strongest models)!
The votes will then be gathered to create an Elo ranking of the safest models with respect to PII.
In the future, with the support of the community, this arena could inform safety choices that company make, when choosing models and guardrails on their resistance to adversarial attacks.
It's also a good way to easily demonstrate the limitations of current systems!
Check out the arena: lighthouzai/guardrails-arena
Learn more in the blog: https://huggingface.co/blog/arena-lighthouz
clefourrier
posted
an
update
10 months ago
Post
🔥 New multimodal leaderboard on the hub: ConTextual!
Many situations require models to parse images containing text: maps, web pages, real world pictures, memes, ... 🖼️
So how do you evaluate performance on this task?
The ConTextual team introduced a brand new dataset of instructions and images, to test LMM (large multimodal models) reasoning capabilities, and an associated leaderboard (with a private test set).
This is super exciting imo because it has the potential to be a good benchmark both for multimodal models and for assistants' vision capabilities, thanks to the instructions in the dataset.
Congrats to @rohan598 , @hbXNov , @kaiweichang and @violetpeng !!
Learn more in the blog: https://huggingface.co/blog/leaderboard-contextual
Leaderboard: ucla-contextual/contextual_leaderboard
Many situations require models to parse images containing text: maps, web pages, real world pictures, memes, ... 🖼️
So how do you evaluate performance on this task?
The ConTextual team introduced a brand new dataset of instructions and images, to test LMM (large multimodal models) reasoning capabilities, and an associated leaderboard (with a private test set).
This is super exciting imo because it has the potential to be a good benchmark both for multimodal models and for assistants' vision capabilities, thanks to the instructions in the dataset.
Congrats to @rohan598 , @hbXNov , @kaiweichang and @violetpeng !!
Learn more in the blog: https://huggingface.co/blog/leaderboard-contextual
Leaderboard: ucla-contextual/contextual_leaderboard
clefourrier
updated
2
datasets
10 months ago