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.
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.
Major update on the Talking to Chatbots dataset! Expanded the 'wrapped' dataset (one row per chat) to 2.86k records, and the 'unwrapped' version (one row per conversation turn) to 11k records. The main source is my ChatGPT archive with nearly 2 years of chats. It is still a work in progress as I incorporate chats from other sources and qualitative metrics (SCBN) for responses.
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.
Groundbreaking Research Alert: Rethinking RAG with Cache-Augmented Generation (CAG)
Researchers from National Chengchi University and Academia Sinica have introduced a paradigm-shifting approach that challenges the conventional wisdom of Retrieval-Augmented Generation (RAG).
Instead of the traditional retrieve-then-generate pipeline, their innovative Cache-Augmented Generation (CAG) framework preloads documents and precomputes key-value caches, eliminating the need for real-time retrieval during inference.
Technical Deep Dive: - CAG preloads external knowledge and precomputes KV caches, storing them for future use - The system processes documents only once, regardless of subsequent query volume - During inference, it loads the precomputed cache alongside user queries, enabling rapid response generation - The cache reset mechanism allows efficient handling of multiple inference sessions through strategic token truncation
Performance Highlights: - Achieved superior BERTScore metrics compared to both sparse and dense retrieval RAG systems - Demonstrated up to 40x faster generation times compared to traditional approaches - Particularly effective with both SQuAD and HotPotQA datasets, showing robust performance across different knowledge tasks
Why This Matters: The approach significantly reduces system complexity, eliminates retrieval latency, and mitigates common RAG pipeline errors. As LLMs continue evolving with expanded context windows, this methodology becomes increasingly relevant for knowledge-intensive applications.
🚀 Unlock the power of a completely free, unlimited multilingual API! 🌐 The Lightweight Embeddings API offers state-of-the-art text and image embeddings, advanced reranking, and seamless support for over 100 languages — no limits, no restrictions. 🌟 Try it now: lamhieu/lightweight-embeddings
Excited to share insights from Walmart's groundbreaking semantic search system that revolutionizes e-commerce product discovery!
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results: The system showed significant improvements in both offline metrics and live experiments, with: - +2.84% improvement in NDCG@10 for human evaluation - +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
Groundbreaking Research Alert: Revolutionizing Document Ranking with Long-Context LLMs
Researchers from Renmin University of China and Baidu Inc . have introduced a novel approach to document ranking that challenges conventional sliding window methods. Their work demonstrates how long-context Large Language Models can process up to 100 documents simultaneously, achieving superior performance while reducing API costs by 50%.
Key Technical Innovations: - Full ranking strategy enables processing all passages in a single inference - Multi-pass sliding window approach for comprehensive listwise label construction - Importance-aware learning objective that prioritizes top-ranked passage IDs - Support for context lengths up to 128k tokens using models like LLaMA 3.1-8B-Instruct
Performance Highlights: - 2.2 point improvement in NDCG@10 metrics - 29.3% reduction in latency compared to traditional methods - Significant API cost savings through elimination of redundant passage processing
Under the hood, the system leverages advanced long-context LLMs to perform global interactions among passages, enabling more nuanced relevance assessment. The architecture incorporates a novel importance-aware loss function that assigns differential weights based on passage ranking positions.
The research team's implementation demonstrated remarkable versatility across multiple datasets, including TREC DL and BEIR benchmarks. Their fine-tuned model, RankMistral, showcases the practical viability of full ranking approaches in production environments.
This advancement marks a significant step forward in information retrieval systems, offering both improved accuracy and computational efficiency. The implications for search engines and content recommendation systems are substantial.
Just finally ... added my HF backup tool to an HF repo... after two years roughly of making this - It's inspired KINDA by Camenduru but his one stopped working and i wish i had the original code for it so i could reformat the A111 extension he had...
Becausei 'm TRYING to make an A111 extension and maybe a custom comfyUI node:
Exciting News in AI: JinaAI Releases JINA-CLIP-v2!
The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal:
🚀 Technical Highlights: - Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder - Supports 89 languages with 8,192 token context length - Processes images up to 512×512 pixels with 14×14 patch size - Implements FlashAttention2 for text and xFormers for vision processing - Uses Matryoshka Representation Learning for efficient vector storage
⚡️ Under The Hood: - Multi-stage training process with progressive resolution scaling (224→384→512) - Contrastive learning using InfoNCE loss in both directions - Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs - Incorporates specialized datasets for document understanding, scientific graphs, and infographics - Uses hard negative mining with 7 negatives per positive sample
📊 Performance: - Outperforms previous models on visual document retrieval (52.65% nDCG@5) - Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark - Strong multilingual performance across 30 languages - Maintains performance even with 75% dimension reduction (256D vs 1024D)
🎯 Key Innovation: The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems!
Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!
Fascinating insights from @Pinterest 's latest research on improving feature interactions in recommendation systems!
Pinterest's engineering team has tackled a critical challenge in their Homefeed ranking system that serves 500M+ monthly active users. Here's what makes their approach remarkable:
>> Technical Deep Dive
Architecture Overview • The ranking model combines dense features, sparse features, and embedding features to represent users, Pins, and context • Sparse features are processed using learnable embeddings with size based on feature cardinality • User sequence embeddings are generated using a transformer architecture processing past engagements
Feature Processing Pipeline • Dense features undergo normalization for numerical stability • Sparse and embedding features receive L2 normalization • All features are concatenated into a single feature embedding
Key Innovations • Implemented parallel MaskNet layers with 3 blocks • Used projection ratio of 2.0 and output dimension of 512 • Stacked 4 DCNv2 layers on top for higher-order interactions
Performance Improvements • Achieved +1.42% increase in Homefeed Save Volume • Boosted Overall Time Spent by +0.39% • Maintained memory consumption increase to just 5%
>> Industry Constraints Addressed
Memory Management • Optimized for 60% GPU memory utilization • Prevented OOM errors while maintaining batch size efficiency
Latency Optimization • Removed input-output concatenation before MLP • Reduced hidden layer sizes in MLP • Achieved zero latency increase while improving performance
System Stability • Ensured reproducible results across retraining • Maintained model stability across different data distributions • Successfully deployed in production environment
This work brilliantly demonstrates how to balance academic innovations with real-world industrial constraints. Kudos to the Pinterest team!
Exciting breakthrough in AI: @Meta's new Byte Latent Transformer (BLT) revolutionizes language models by eliminating tokenization!
The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special:
>> Key Innovations Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models.
Three-Component Architecture: • Lightweight Local Encoder that converts bytes to patch representations • Powerful Global Latent Transformer that processes patches • Local Decoder that converts patches back to bytes
>> Technical Advantages • Matches performance of Llama 3 at 8B parameters while being more efficient • Superior handling of non-English languages and rare character sequences • Remarkable 99.9% accuracy on spelling tasks • Better scaling properties than token-based models
>> Under the Hood The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs.
This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
