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Nicolay Rusnachenko

nicolay-r
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https://nicolay-r.github.io/

AI & ML interests

Information Retrieval・Medical Multimodal NLP (πŸ–Ό+πŸ“) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

Recent Activity

posted an update about 9 hours ago
πŸ“’ Qwen so far released the 2.5-MAX that claims to outperform DeepSeek-R1. And here is how you can start applying it for handling CSV / JSONL data. The model is compatible with OpenAI API so here is my wrapper for it: 🌌 https://github.com/nicolay-r/nlp-thirdgate/blob/master/llm/openai_156.py πŸš€ All you have to do is to set base-url: https://dashscope-intl.aliyuncs.com/compatible-mode/v1 and API key of the platform. ↗️ Below is the link to the complete example (see screenshot): https://github.com/nicolay-r/nlp-thirdgate/blob/master/tutorials/llm_qwen_25_max_chat.sh πŸ“° Source: https://www.alibabacloud.com/help/en/model-studio/developer-reference/what-is-qwen-llm πŸ“Ί Official Sandbox Demo: https://huggingface.co/spaces/Qwen/Qwen2.5-Max-Demo πŸ“œ Paper: https://arxiv.org/abs/2412.15115
reacted to singhsidhukuldeep's post with πŸš€ about 12 hours ago
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 JingzeShi's post with πŸ€— about 14 hours ago
Welcome to the Doge Face Open Source Community! πŸš€ Our goal is to explore the foundation of embodied intelligence for the next two years, which is indispensable – small language models. πŸ”¬ We aim to open-source code and documentation to give everyone more time to slack off while working or studying! πŸ€— πŸ‘‰ Repository name on Github: https://github.com/SmallDoges/small-doge πŸ‘‰ Organization name on Hugging Face: https://huggingface.co/SmallDoge
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