1 citations · 1 across the 2 of their papers we have counts for
10 papers
Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding
Sungkyun Kim, Jaemin Kim, Dogyung Yoon +3
LLMs have low GPU efficiency and high latency due to autoregressive decoding. Speculative decoding (SD) mitigates this using a small draft model to speculatively generate multiple…
From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
Gopi Krishnan Rajbahadur, Gustavo A. Oliva, Dayi Lin +2
The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While buil…
Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy
Hamed Taherkhani, Jiho Shin, Muhammad Ammar Tahir +3
Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. Ho…
Benchmarking CXR Foundation Models With Publicly Available MIMIC-CXR and NIH-CXR14 Datasets
Jiho Shin, Dominic Marshall, Matthieu Komorowski
Recent foundation models have demonstrated strong performance in medical image representation learning, yet their comparative behaviour across datasets remains underexplored. This…
Retrieval-Augmented Test Generation: How Far Are We?
Jiho Shin, Nima Shiri Harzevili, Reem Aleithan +2
Retrieval Augmented Generation (RAG) has advanced software engineering tasks but remains underexplored in unit test generation. To bridge this gap, we investigate the efficacy of R…
BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma, Jiho Shin, Leuson Da Silva +5
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evalu…