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 11 minutes ago

Excited to share groundbreaking research from @Baidu_Inc on enterprise information search! The team has developed EICopilot, a revolutionary agent-based solution that transforms how we explore enterprise data in large-scale knowledge graphs. >> Technical Innovation EICopilot leverages Large Language Models to interpret natural language queries and automatically generates Gremlin scripts for enterprise data exploration. The system processes hundreds of millions of nodes and billions of edges in real-time, handling complex enterprise relationships with remarkable precision. Key Technical Components: - Advanced data pre-processing pipeline that builds vector databases of representative queries - Novel query masking strategy that significantly improves intent recognition - Comprehensive reasoning pipeline combining Chain-of-Thought with In-context learning - Named Entity Recognition and Natural Language Processing Customization for precise entity matching - Schema Linking Module for efficient graph database query generation >> Performance Metrics The results are impressive - EICopilot achieves a syntax error rate as low as 10% and execution correctness up to 82.14%. The system handles 5000+ daily active users, demonstrating its robustness in real-world applications. >> Implementation Details The system uses Apache TinkerPop for graph database construction and employs sophisticated disambiguation processes, including anaphora resolution and entity retrieval. The architecture includes both offline and online phases, with continuous learning from user interactions to improve query accuracy. Kudos to the research team from Baidu Inc., South China University of Technology, and other collaborating institutions for this significant advancement in enterprise information retrieval technology.

posted an update 1 day ago

Exciting breakthrough in AI: AirRAG - A Novel Approach to Retrieval Augmented Generation! Researchers from Alibaba Cloud have developed a groundbreaking framework that significantly improves how AI systems reason and retrieve information. AirRAG introduces five fundamental reasoning actions that work together to create more accurate and comprehensive responses. >> Key Technical Innovations: - Implements Monte Carlo Tree Search (MCTS) for exploring diverse reasoning paths - Utilizes five core actions: System Analysis, Direct Answer, Retrieval-Answer, Query Transformation, and Summary-Answer - Features self-consistency verification and process-supervised reward modeling - Achieves superior performance across complex QA datasets like HotpotQA, MuSiQue, and 2WikiMultiHopQA >> Under the Hood: The system expands solution spaces through tree-based search, allowing for multiple reasoning paths to be explored simultaneously. The framework implements computationally optimal strategies, applying more resources to key actions while maintaining efficiency. >> Results Speak Volumes: - Outperforms existing RAG methods by over 10% on average - Shows remarkable scalability with increasing inference computation - Demonstrates exceptional flexibility in integrating with other advanced technologies This research represents a significant step forward in making AI systems more capable of complex reasoning tasks. The team's innovative approach combines human-like reasoning with advanced computational techniques, setting new benchmarks in the field.

posted an update 3 days ago

Groundbreaking Research Alert: Can Large Language Models Really Understand Personal Preferences? A fascinating new study from researchers at University of Notre Dame, Xi'an Jiaotong University, and Université de Montréal introduces PERRECBENCH - a novel benchmark for evaluating how well Large Language Models (LLMs) understand user preferences in recommendation systems. Key Technical Insights: - The benchmark eliminates user rating bias and item quality factors by using relative ratings and grouped ranking approaches - Implements three distinct ranking methods: pointwise rating prediction, pairwise comparison, and listwise ranking - Evaluates 19 state-of-the-art LLMs including Claude-3.5, GPT-4, Llama-3, Mistral, and Qwen models - Uses Kendall's tau correlation to measure ranking accuracy - Incorporates BM25 retriever with configurable history items (k=4 by default) Notable Findings: - Current LLMs struggle with true personalization, achieving only moderate correlation scores - Larger models don't always perform better - challenging conventional scaling laws - Pairwise and listwise ranking methods outperform pointwise approaches - Open-source models like Mistral-123B and Llama-3-405B compete well with proprietary models - Weight merging strategy shows promise for improving personalization capabilities The research reveals that while LLMs excel at many tasks, they still face significant challenges in understanding individual user preferences. This work opens new avenues for improving personalized recommendation systems and highlights the importance of developing better evaluation methods. A must-read for anyone interested in LLMs, recommender systems, or personalization technology. The team has made their benchmark and code publicly available for further research.

View all activity

Organizations

Posts 123

Post

Excited to share groundbreaking research from @Baidu_Inc on enterprise information search! The team has developed EICopilot, a revolutionary agent-based solution that transforms how we explore enterprise data in large-scale knowledge graphs.

>> Technical Innovation
EICopilot leverages Large Language Models to interpret natural language queries and automatically generates Gremlin scripts for enterprise data exploration. The system processes hundreds of millions of nodes and billions of edges in real-time, handling complex enterprise relationships with remarkable precision.

Key Technical Components:
- Advanced data pre-processing pipeline that builds vector databases of representative queries
- Novel query masking strategy that significantly improves intent recognition
- Comprehensive reasoning pipeline combining Chain-of-Thought with In-context learning
- Named Entity Recognition and Natural Language Processing Customization for precise entity matching
- Schema Linking Module for efficient graph database query generation

>> Performance Metrics
The results are impressive - EICopilot achieves a syntax error rate as low as 10% and execution correctness up to 82.14%. The system handles 5000+ daily active users, demonstrating its robustness in real-world applications.

>> Implementation Details
The system uses Apache TinkerPop for graph database construction and employs sophisticated disambiguation processes, including anaphora resolution and entity retrieval. The architecture includes both offline and online phases, with continuous learning from user interactions to improve query accuracy.

Kudos to the research team from Baidu Inc., South China University of Technology, and other collaborating institutions for this significant advancement in enterprise information retrieval technology.

Post

814

Exciting breakthrough in AI: AirRAG - A Novel Approach to Retrieval Augmented Generation!

Researchers from Alibaba Cloud have developed a groundbreaking framework that significantly improves how AI systems reason and retrieve information. AirRAG introduces five fundamental reasoning actions that work together to create more accurate and comprehensive responses.

>> Key Technical Innovations:
- Implements Monte Carlo Tree Search (MCTS) for exploring diverse reasoning paths
- Utilizes five core actions: System Analysis, Direct Answer, Retrieval-Answer, Query Transformation, and Summary-Answer
- Features self-consistency verification and process-supervised reward modeling
- Achieves superior performance across complex QA datasets like HotpotQA, MuSiQue, and 2WikiMultiHopQA

>> Under the Hood:
The system expands solution spaces through tree-based search, allowing for multiple reasoning paths to be explored simultaneously. The framework implements computationally optimal strategies, applying more resources to key actions while maintaining efficiency.

>> Results Speak Volumes:
- Outperforms existing RAG methods by over 10% on average
- Shows remarkable scalability with increasing inference computation
- Demonstrates exceptional flexibility in integrating with other advanced technologies

This research represents a significant step forward in making AI systems more capable of complex reasoning tasks. The team's innovative approach combines human-like reasoning with advanced computational techniques, setting new benchmarks in the field.

View all posts

spaces 1

Running

📉

Posts Leaderboard

models

None public yet

datasets

None public yet