5 papers
Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation
Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg +4
Small open-source code models that power IDE autocomplete still emit hallucinated Fill-in-the-Middle (FIM) completions: syntactically natural calls to methods, parameters, variable…
Delulu: A Verified Multi-Lingual Benchmark for Code Hallucination Detection in Fill-in-the-Middle Tasks
Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg +4
Large Language Models for code generation frequently produce hallucinations in Fill-in-the-Middle (FIM) tasks -- plausible but incorrect completions such as invented API methods, i…
NeedleDB: A Generative-AI Based System for Accurate and Efficient Image Retrieval using Complex Natural Language Queries
Mahdi Erfanian, Abolfazl Asudeh
We demonstrate NeedleDB, an open-source, deployment-ready database system for answering complex natural language queries over image data. Unlike existing approaches that rely on co…
Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries
Mahdi Erfanian, Mohsen Dehghankar, Abolfazl Asudeh
Multi-modal datasets, like those involving images, often miss the detailed descriptions that properly capture the rich information encoded in each item. This makes answering comple…
An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks
Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh
Despite their tremendous success and versatility, Deep Neural Networks (DNNs) such as Large Language Models (LLMs) suffer from inference inefficiency and rely on advanced computati…