Assigning higher redundancy to semantically important query features reduces retrieval error probability under token erasures, via multivariate Gaussian approximations of similarity margins and supporting numerical results.
Enhancing llm factual accuracy with rag to counter hallucinations: A case study on domain-specific queries in private knowledge-bases
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
An external zero-shot monitor detects nine unsafe reasoning behaviors in LLMs at 87% step-level accuracy with low false positives and low latency.
Representative Stochastic ranking achieves near-parity average exposure on the TREC 2022 Fair Ranking Dataset, with generation demographic parity closely tracking retrieval exposure.
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
HPC-LLM fine-tunes Llama 3.1 8B via QLoRA on 9k-24k HPC examples and adds dense retrieval to deliver practical support for job scheduling, MPI, and GPU workflows, approaching the performance of larger general models at lower memory and latency cost.
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
-
Context-Aware Search and Retrieval Under Token Erasure
Assigning higher redundancy to semantically important query features reduces retrieval error probability under token erasures, via multivariate Gaussian approximations of similarity margins and supporting numerical results.
-
Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models
An external zero-shot monitor detects nine unsafe reasoning behaviors in LLMs at 87% step-level accuracy with low false positives and low latency.
-
Towards FairRAG: Preventing Representational Harm in Retrieval-Augmented Generation by Enforcing Fair Exposure at Retrieval Time
Representative Stochastic ranking achieves near-parity average exposure on the TREC 2022 Fair Ranking Dataset, with generation demographic parity closely tracking retrieval exposure.
-
Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
-
HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support
HPC-LLM fine-tunes Llama 3.1 8B via QLoRA on 9k-24k HPC examples and adds dense retrieval to deliver practical support for job scheduling, MPI, and GPU workflows, approaching the performance of larger general models at lower memory and latency cost.
-
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.