P2R builds LLM-generated structured expertise profiles and uses coarse-to-fine hybrid retrieval plus rubric-scoring committees to match papers to reviewers, outperforming paper-to-paper baselines on NeurIPS, SIGIR, and SciRepEval.
A comprehensive survey on vector database: Storage and retrieval technique, challenge.Computing Research Repository, abs/2310.11703
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Benchmark study shows DCO methods for vector similarity search are not reliable silver bullets due to high sensitivity to data properties and hardware, making them unsuitable for production deployment.
FINER-SQL boosts 3B-parameter small language models to 67.73% and 85% execution accuracy on BIRD and Spider benchmarks via dense memory and atomic rewards in group relative policy optimization, matching larger LLMs at lower latency.
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
Presents a training-free personalization toolkit for LVLMs that extracts features via vision foundation models, applies RAG for instance retrieval, and uses visual prompting for multi-concept adaptation on images and videos, claiming SOTA results on a new real-world benchmark.
A RAG-based virtual assistant was developed and evaluated to deliver accurate, context-specific responses for students navigating university project regulations.
citing papers explorer
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Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching
P2R builds LLM-generated structured expertise profiles and uses coarse-to-fine hybrid retrieval plus rubric-scoring committees to match papers to reviewers, outperforming paper-to-paper baselines on NeurIPS, SIGIR, and SciRepEval.
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Distance Comparison Operations Are Not Silver Bullets in Vector Similarity Search: A Benchmark Study on Their Merits and Limits
Benchmark study shows DCO methods for vector similarity search are not reliable silver bullets due to high sensitivity to data properties and hardware, making them unsuitable for production deployment.
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FINER-SQL: Boosting Small Language Models for Text-to-SQL
FINER-SQL boosts 3B-parameter small language models to 67.73% and 85% execution accuracy on BIRD and Spider benchmarks via dense memory and atomic rewards in group relative policy optimization, matching larger LLMs at lower latency.
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Towards Explorative IRBL: Combining Semantic Retrieval with LLM-driven Iterative Code Exploration
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
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Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
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Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Presents a training-free personalization toolkit for LVLMs that extracts features via vision foundation models, applies RAG for instance retrieval, and uses visual prompting for multi-concept adaptation on images and videos, claiming SOTA results on a new real-world benchmark.
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Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
A RAG-based virtual assistant was developed and evaluated to deliver accurate, context-specific responses for students navigating university project regulations.