18 15 221

Harpreet Sahota PRO

harpreetsahota

AI & ML interests

Deep learning, laguage models, prompt engineering, agents, multi-agent systems

Recent Activity

liked a Space 13 days ago

stabilityai/stable-diffusion-3.5-large

updated a dataset 15 days ago

harpreetsahota/marvel-bobbleheads

liked a dataset 16 days ago

harpreetsahota/marvel-bobbleheads

View all activity

Articles

The CVPR Survival Guide: Discovering Research That's Interesting to YOU!

Jun 14

• 9

Organizations

Posts 3

Post

2087

The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.

I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.

The dataset consists of the following fields:

- An image of the first page of the paper
- title: The title of the paper
- authors_list: The list of authors
- abstract: The abstract of the paper
- arxiv_link: Link to the paper on arXiv
- other_link: Link to the project page, if found
- category_name: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)
- all_categories: All categories this paper falls into, according to arXiv taxonomy
- keywords: Extracted using GPT-4o

Here's how I created the dataset 👇🏼

Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).

This dataset was built using the following steps:

- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract

Voxel51/CVPR_2024_Papers

Post

google/gemma-7b-it is super good!

I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.

I've got a notebook here, which is kind of a framework for vibe-checking LLMs.

In this notebook, I take Gemma for a spin on a variety of prompts:
• [nonsensical tokens]( harpreetsahota/diverse-token-sampler
• [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
• [summarization ability]( lighteval/summarization)
• [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
• [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)

I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.

I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.

Cheers:

You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing

View all posts