PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.
2408.13296 , archivePrefix =
8 Pith papers cite this work. Polarity classification is still indexing.
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SecureGate reduces PII leakage up to 31.66X in federated LLM fine-tuning via token-gated dual LoRA adapters while preserving utility and achieving perfect routing reliability.
A data-augmentation framework for conjoint analysis integrates LLM-generated data with human responses to yield consistent, asymptotically normal estimators and reported cost savings of 24.9-79.8% in two empirical studies.
RAG is more effective and cost-efficient than fine-tuning for industrial QA adaptation on automotive datasets.
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
RAG-enhanced LLMs show generally positive effects on automated test generation and code inspection by supplying supplementary context that reduces hallucinations.
A domain-specific LLM for TB care in South Africa, created by fine-tuning BioMistral-7B with QLoRA and GraphRAG on local guidelines, shows improved contextual alignment over the base model.
citing papers explorer
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PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.
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SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs
SecureGate reduces PII leakage up to 31.66X in federated LLM fine-tuning via token-gated dual LoRA adapters while preserving utility and achieving perfect routing reliability.
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Large Language Models for Market Research: A Data-augmentation Approach
A data-augmentation framework for conjoint analysis integrates LLM-generated data with human responses to yield consistent, asymptotically normal estimators and reported cost savings of 24.9-79.8% in two empirical studies.
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Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications
RAG is more effective and cost-efficient than fine-tuning for industrial QA adaptation on automotive datasets.
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DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
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From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
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Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation
RAG-enhanced LLMs show generally positive effects on automated test generation and code inspection by supplying supplementary context that reduces hallucinations.
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Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa
A domain-specific LLM for TB care in South Africa, created by fine-tuning BioMistral-7B with QLoRA and GraphRAG on local guidelines, shows improved contextual alignment over the base model.