24 citations · 58 across the 50 of their papers we have counts for
6 papers · 1 filter
Enhancing Small Language Models for Power Outage Report Generation via Minimum Risk Training
Hung Phan, Waqwoya Abebe, Youssef Hussein +3
Minimum Risk Training (MRT) enables neural machine translation models to directly optimize sequence-level evaluation metrics instead of relying only on token- level maximum-likelih…
Interpretable Adaptive Sampling for LLM Test-Time Scaling
Mobina Kashaniyan, Ali Jannesari
Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that spend the same compute on eas…
POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation
Hung Phan, Aniroop Naladala, Dubey Avanindra +3
Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict…
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +5
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sust…
ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms
Mohammad Pivezhandi, Mahdi Banisharif, Abusayeed Saifullah +1
Dynamic voltage and frequency scaling (DVFS) and task-to-core allocation are critical for thermal management and balancing energy and performance in embedded systems. Existing appr…
VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +8
Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show promise for Hardware Description Lan…