Sleeping
🐨
CFPB Assistant
CFPB Assistant using vectara-agentic
Vectara
company
AI & ML interests
retrieval augmented generation, grounded generation, large language models, LLMs, question answering, chatbot
Recent Activity
vectara's activity
ofermend
updated
6
Spaces
4 days ago
Sleeping
2
🐨
EV Assistant
EV Assistant using vectara-agentic
Running
11
🐨
Finance assistant
Finance chatbot using vectara-agentic
Running
5
🐨
Legal Assistant
Legal Assistant using vectara-agentic
Sleeping
2
🐨
Justice Harvard
Teacher Assistant for Justice Harvard using vectara-agentic
Sleeping
5
🐨
Hacker News chat
chatbot with HN data using vectara-agentic
Miaoran000
updated
a
Space
5 days ago
Miaoran000
updated
a
dataset
13 days ago
clefourrier
authored
a
paper
19 days ago
nthakur
authored
a
paper
about 1 month ago
nthakur
authored
a
paper
4 months ago
Post
1824
I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate.
vectara/Hallucination-evaluation-leaderboard
vectara/Hallucination-evaluation-leaderboard
- 1 reply
clefourrier
authored
2
papers
6 months ago
Post
1754
If you are a debate fan or did this as an extracurricular activity as a kid, you might have fun with this demo - debate bot. Debate against AI/RAG:
vectara/debate-bot
vectara/debate-bot
-
- 4 replies
Post
3289
🦢 The SWIM-IR dataset contains 29 million text-retrieval training pairs across 27 diverse languages. It is one of the largest synthetic multilingual datasets generated using PaLM 2 on Wikipedia! 🔥🔥
SWIM-IR dataset contains three subsets :
- Cross-lingual:
- Monolingual:
- Indic Cross-lingual:
Check it out:
https://huggingface.co/collections/nthakur/swim-ir-dataset-662ddaecfc20896bf14dd9b7
SWIM-IR dataset contains three subsets :
- Cross-lingual:
nthakur/swim-ir-cross-lingual- Monolingual:
nthakur/swim-ir-monolingual- Indic Cross-lingual:
nthakur/indic-swim-ir-cross-lingualCheck it out:
https://huggingface.co/collections/nthakur/swim-ir-dataset-662ddaecfc20896bf14dd9b7
clefourrier
posted
an
update
8 months ago
Post
5446
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
4427
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
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...