Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
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2506.15674
•
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SomosNLP
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Democratizar el PLN en español e incentivar su aplicación para generar impacto social 💛
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haritzpuerto
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7 days ago
suchirsalhan
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about 1 month ago
mariagrandury
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about 1 month ago
dvilasuero
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about 2 months ago
Post
2729
Super excited to launch Hugging Face Sheets: Spreadsheets meet AI and unstructured data.
A few months ago, we started imagining new ways to build and transform datasets with the latest open-source models.
Today, I'm thrilled to introduce our first step in this direction.
In a nutshell:
📁 Effortlessly run prompts and models over your data.
🌐 Agentic search for accuracy and real-time information.
🖼️ Familiar, minimalistic interface for interacting with data.
🎯 Human feedback 2.0: Your input directly improves generated data.
💯 Access hundreds of open models and leading inference providers.
Go to this space to try it out!
aisheets/sheets
Leave your questions below, we're just getting started!
A few months ago, we started imagining new ways to build and transform datasets with the latest open-source models.
Today, I'm thrilled to introduce our first step in this direction.
In a nutshell:
📁 Effortlessly run prompts and models over your data.
🌐 Agentic search for accuracy and real-time information.
🖼️ Familiar, minimalistic interface for interacting with data.
🎯 Human feedback 2.0: Your input directly improves generated data.
💯 Access hundreds of open models and leading inference providers.
Go to this space to try it out!
aisheets/sheets
Leave your questions below, we're just getting started!
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- 3 replies
haritzpuerto
posted
an
update
2 months ago
Post
319
📜 Accepted at ACL 2025! Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs
We propose to fine-tune LLMs to generate diverse chains of thought (DCoT) in a single inference step. This enables within-inference refinement of the cots, no external feedback needed!
🔗 https://arxiv.org/abs/2407.03181
We propose to fine-tune LLMs to generate diverse chains of thought (DCoT) in a single inference step. This enables within-inference refinement of the cots, no external feedback needed!
🔗 https://arxiv.org/abs/2407.03181
How use the dataset to train my model GPT
2
#1 opened 3 months ago
by
luisaarias
ouhenio
updated
a
Space
3 months ago
suchirsalhan
authored
a
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4 months ago
plaguss
authored
a
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6 months ago
gabrielmbmb
authored
a
paper
6 months ago
mariagrandury
updated
3
collections
6 months ago
haritzpuerto
posted
an
update
6 months ago
Post
642
I just got my first ChatGPT review on ARR! 😅 Any advice on how to prove it's AI-generated? Thanks!
- 3 replies
haritzpuerto
posted
an
update
6 months ago
Post
1484
I'm excited to announce that my internship paper at Parameter Lab was accepted to Findings of #NAACL2025 🎉
TLDR: Stating an LLM was trained on a sentence might not be possible 😥 , but it is possible for large enough amounts of tokens, such as long documents or multiple documents! 🤯
Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models (2411.00154)
🔗 https://github.com/parameterlab/mia-scaling
TLDR: Stating an LLM was trained on a sentence might not be possible 😥 , but it is possible for large enough amounts of tokens, such as long documents or multiple documents! 🤯
Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models (2411.00154)
🔗 https://github.com/parameterlab/mia-scaling
nataliaElv
posted
an
update
6 months ago
Post
1532
New chapter in the Hugging Face NLP course! 🤗 🚀
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
Any feedback for improvements welcome!
https://huggingface.co/learn/nlp-course/chapter10
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
Any feedback for improvements welcome!
https://huggingface.co/learn/nlp-course/chapter10
nataliaElv
posted
an
update
7 months ago
Post
565
Do you want to easily save annotations to a Dataset in the Hub?
In the last version of Argilla (v2.6.0), you can export your data directly from the UI to the Hub.
Check all the changes and update to the latest version: https://github.com/argilla-io/argilla/releases/tag/v2.6.0
In the last version of Argilla (v2.6.0), you can export your data directly from the UI to the Hub.
Check all the changes and update to the latest version: https://github.com/argilla-io/argilla/releases/tag/v2.6.0
nataliaElv
posted
an
update
7 months ago
Post
1674
If you are still wondering how the FineWeb2 annotations are done, how to follow the guidelines or how Argilla works, this is your video!
I go through a few samples of the FineWeb2 dataset and classify them based on their educational content. Check it out!
https://www.youtube.com/watch?v=_-ORB4WAVGU
I go through a few samples of the FineWeb2 dataset and classify them based on their educational content. Check it out!
https://www.youtube.com/watch?v=_-ORB4WAVGU
nataliaElv
posted
an
update
8 months ago
Post
1310
How do your annotations for FineWeb2 compare to your teammates'?
I started contributing some annotations to the FineWeb2 collaborative annotation sprint and I wanted to know if my labelling trends were similar to those of my teammates.
I did some analysis and I wasn't surprised to see that I'm being a bit harsher on my evaluations than my mates 😂
Do you want to see how your annotations compare to others?
👉 Go to this Gradio space: nataliaElv/fineweb2_compare_my_annotations
✍️ Enter the dataset that you've contributed to and your Hugging Face username.
How were your results?
- Contribute some annotations: data-is-better-together/fineweb-c
- Join your language channel in Rocket chat: https://huggingface.co/spaces/HuggingFaceFW/discussion
I started contributing some annotations to the FineWeb2 collaborative annotation sprint and I wanted to know if my labelling trends were similar to those of my teammates.
I did some analysis and I wasn't surprised to see that I'm being a bit harsher on my evaluations than my mates 😂
Do you want to see how your annotations compare to others?
👉 Go to this Gradio space: nataliaElv/fineweb2_compare_my_annotations
✍️ Enter the dataset that you've contributed to and your Hugging Face username.
How were your results?
- Contribute some annotations: data-is-better-together/fineweb-c
- Join your language channel in Rocket chat: https://huggingface.co/spaces/HuggingFaceFW/discussion