Kuldeep Singh Sidhu

singhsidhukuldeep

https://singhsidhukuldeep.github.io

AI & ML interests

😃 TOP 3 on HuggingFace for posts 🤗 Seeking contributors for a completely open-source 🚀 Data Science platform! singhsidhukuldeep.github.io

Recent Activity

posted an update about 13 hours ago

Groundbreaking Survey on Large Language Models in Recommendation Systems! Just read a comprehensive survey that maps out how LLMs are revolutionizing recommender systems. The authors have meticulously categorized existing approaches into two major paradigms: Discriminative LLMs for Recommendation: - Leverages BERT-like models for understanding user-item interactions - Uses fine-tuning and prompt tuning to adapt pre-trained models - Excels at tasks like user representation learning and ranking Generative LLMs for Recommendation: - Employs GPT-style models to directly generate recommendations - Implements innovative techniques like in-context learning and zero-shot recommendation - Supports natural language interaction and explanation generation Key Technical Insights: - Novel taxonomy of modeling paradigms: LLM Embeddings + RS, LLM Tokens + RS, and LLM as RS - Integration methods spanning from simple prompting to sophisticated instruction tuning - Hybrid approaches combining collaborative filtering with LLM capabilities - Advanced prompt engineering techniques for controlled recommendation generation Critical Challenges Identified: - Position and popularity bias in LLM recommendations - Limited context length affecting user history processing - Need for better evaluation metrics for generative recommendations - Controlled output generation and personalization challenges This work opens exciting possibilities for next-gen recommendation systems while highlighting crucial areas for future research.

posted an update 3 days ago

Groundbreaking Research Alert: Correctness ≠ Faithfulness in RAG Systems Fascinating new research from L3S Research Center, University of Amsterdam, and TU Delft reveals a critical insight into Retrieval Augmented Generation (RAG) systems. The study exposes that up to 57% of citations in RAG systems could be unfaithful, despite being technically correct. >> Key Technical Insights: Post-rationalization Problem The researchers discovered that RAG systems often engage in "post-rationalization" - where models first generate answers from their parametric memory and then search for supporting evidence afterward. This means that while citations may be correct, they don't reflect the actual reasoning process. Experimental Design The team used Command-R+ (104B parameters) with 4-bit quantization on NVIDIA A100 GPU, testing on the NaturalQuestions dataset. They employed BM25 for initial retrieval and ColBERT v2 for reranking. Attribution Framework The research introduces a comprehensive framework for evaluating RAG systems across multiple dimensions: - Citation Correctness: Whether cited documents support the claims - Citation Faithfulness: Whether citations reflect actual model reasoning - Citation Appropriateness: Relevance and meaningfulness of citations - Citation Comprehensiveness: Coverage of key points Under the Hood The system processes involve: 1. Document relevance prediction 2. Citation prediction 3. Answer generation without citations 4. Answer generation with citations This work fundamentally challenges our understanding of RAG systems and highlights the need for more robust evaluation metrics in AI systems that claim to provide verifiable information.

posted an update 6 days ago

Exciting breakthrough in e-commerce recommendation systems! Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations. >> Key Innovation The framework ingeniously combines three distinct data types: - Visual data to capture product aesthetics and context - Textual information for detailed product features - Graph data to understand complex user-item relationships >> Technical Architecture The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components: Modality Fusion Module - All-Modality Self-Attention (AMSA) for unified representation - Cross-Modality Attention (CMA) mechanism for deep feature integration - Custom FFN adapters to align different modality embeddings Advanced Training Strategy - Curriculum learning approach with three complexity levels - Parameter-Efficient Fine-Tuning using LoRA - Special token system for behavior and item representation >> Real-World Impact The results are remarkable: - 38.25% improvement in Electronics recommendations - 43.09% boost in Sports category accuracy - Significantly higher human evaluation scores compared to traditional methods Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.

View all activity

Organizations

Posts 110

Post

456

Groundbreaking Survey on Large Language Models in Recommendation Systems!

Just read a comprehensive survey that maps out how LLMs are revolutionizing recommender systems. The authors have meticulously categorized existing approaches into two major paradigms:

Discriminative LLMs for Recommendation:
- Leverages BERT-like models for understanding user-item interactions
- Uses fine-tuning and prompt tuning to adapt pre-trained models
- Excels at tasks like user representation learning and ranking

Generative LLMs for Recommendation:
- Employs GPT-style models to directly generate recommendations
- Implements innovative techniques like in-context learning and zero-shot recommendation
- Supports natural language interaction and explanation generation

Key Technical Insights:
- Novel taxonomy of modeling paradigms: LLM Embeddings + RS, LLM Tokens + RS, and LLM as RS
- Integration methods spanning from simple prompting to sophisticated instruction tuning
- Hybrid approaches combining collaborative filtering with LLM capabilities
- Advanced prompt engineering techniques for controlled recommendation generation

Critical Challenges Identified:
- Position and popularity bias in LLM recommendations
- Limited context length affecting user history processing
- Need for better evaluation metrics for generative recommendations
- Controlled output generation and personalization challenges

This work opens exciting possibilities for next-gen recommendation systems while highlighting crucial areas for future research.

Post

1385

Groundbreaking Research Alert: Correctness ≠ Faithfulness in RAG Systems

Fascinating new research from L3S Research Center, University of Amsterdam, and TU Delft reveals a critical insight into Retrieval Augmented Generation (RAG) systems. The study exposes that up to 57% of citations in RAG systems could be unfaithful, despite being technically correct.

>> Key Technical Insights:

Post-rationalization Problem
The researchers discovered that RAG systems often engage in "post-rationalization" - where models first generate answers from their parametric memory and then search for supporting evidence afterward. This means that while citations may be correct, they don't reflect the actual reasoning process.

Experimental Design
The team used Command-R+ (104B parameters) with 4-bit quantization on NVIDIA A100 GPU, testing on the NaturalQuestions dataset. They employed BM25 for initial retrieval and ColBERT v2 for reranking.

Attribution Framework
The research introduces a comprehensive framework for evaluating RAG systems across multiple dimensions:
- Citation Correctness: Whether cited documents support the claims
- Citation Faithfulness: Whether citations reflect actual model reasoning
- Citation Appropriateness: Relevance and meaningfulness of citations
- Citation Comprehensiveness: Coverage of key points

Under the Hood
The system processes involve:
1. Document relevance prediction
2. Citation prediction
3. Answer generation without citations
4. Answer generation with citations

This work fundamentally challenges our understanding of RAG systems and highlights the need for more robust evaluation metrics in AI systems that claim to provide verifiable information.

View all posts

spaces 1

Running

📉

Posts Leaderboard

models

None public yet

datasets

None public yet