Feynman Innovations

ajibawa-2023

AI & ML interests

LLM, RL, DL, ML, AGI. Developing LLMs (preferably fully fine tuned ) for various use cases.

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ajibawa-2023's activity

reacted to their post with β€οΈπŸš€πŸ‘πŸ”₯ 34 minutes ago
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Hi All, I recently released two Audio datasets which are generated using my earlier released dataset: ajibawa-2023/Children-Stories-Collection

First Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection-Large has 5600++ stories in .mp3 format.

Second Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection has 600 stories in .mp3 format.
posted an update 36 minutes ago
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Hi All, I recently released two Audio datasets which are generated using my earlier released dataset: ajibawa-2023/Children-Stories-Collection

First Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection-Large has 5600++ stories in .mp3 format.

Second Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection has 600 stories in .mp3 format.
New activity in ajibawa-2023/Code-290k-ShareGPT about 1 month ago
New activity in ajibawa-2023/Python-Code-23k-ShareGPT about 1 month ago

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#2 opened about 1 month ago by
danfperam
reacted to singhsidhukuldeep's post with πŸ”₯ 2 months ago
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Exciting Research Alert: Revolutionizing Complex Information Retrieval!

A groundbreaking paper from researchers at MIT, AWS AI, and UPenn introduces ARM (Alignment-Oriented LLM-based Retrieval Method), a novel approach to tackle complex information retrieval challenges.

>> Key Innovations

Information Alignment
The method first decomposes queries into keywords and aligns them with available data using both BM25 and embedding similarity, ensuring comprehensive coverage of information needs.

Structure Alignment
ARM employs a sophisticated mixed-integer programming solver to identify connections between data objects, exploring relationships beyond simple semantic matching.

Self-Verification
The system includes a unique self-verification mechanism where the LLM evaluates and aggregates results from multiple retrieval paths, ensuring accuracy and completeness.

>> Performance Highlights

The results are impressive:
- Outperforms standard RAG by up to 5.2 points in execution accuracy on Bird dataset
- Achieves 19.3 points higher F1 scores compared to existing approaches on OTT-QA
- Reduces the number of required LLM calls while maintaining superior retrieval quality

>> Technical Implementation

The system uses a three-step process:
1. N-gram indexing and embedding computation for all data objects
2. Constrained beam decoding for information alignment
3. Mixed-integer programming optimization for structure exploration

This research represents a significant step forward in making complex information retrieval more efficient and accurate. The team's work demonstrates how combining traditional optimization techniques with modern LLM capabilities can solve challenging retrieval problems.
reacted to Tonic's post with πŸ”₯ 2 months ago
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πŸ™‹πŸ»β€β™‚οΈhey there folks ,

Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math

give it a try !