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Technical Papers

Explore our collection of technical papers that delve into the intricacies of ensuring AI performance, alignment, and safety. Gain insights into the cutting-edge methodologies and technologies Trustwise employs to pioneer responsible AI across industries.

Optimizing RAGs: The SHARP Method for Improved Safety…

How SHARP Unlocks Value for Enterprises Deploying Generative AI

Matthew Barker, Avi Saxena, Umang Bhatt

 

Survey of Hallucination in Natural Language Generation

Natural Language Generation (NLG) has improved exponentially in recent years…

Ziwei Ji, Nayeon Lee, Rita Frieske, et al.

 

Seven Failure Points when Engineering RAG

Seven Failure Points When Engineering a Retrieval Augmented Generation System

Scott Barnett, Stefanus Kurniawan, Srikanth

 

From Words to Watts

 

Benchmarking the Energy Costs of Large Language Model Inference

Siddharth Samsi, Dan Zhao, Joseph McDon

 

Fine-tuning Language Models for Factuality

Recent developments in training large language models (LLMs)…

Katherine Tian, Eric Mitchell, Huaxiu Yao, Chr

 

Comprehensive Survey of Hallucination Mitigation…

A Comprehensive Survey of Hallucination Mitigation Techniques in LLMs

S.M Towhidul Islam Tonmoy, S M Mehedi Za

 

Auditing Large Language Models: A Three-Layered Approach

Large language models (LLMs) represent a major advance in artificial intelligence (AI) research.

Jakob Mökander, Jonas Schuett, Hannah

 

A Survey on Evaluation of Large Language Models

Recent developments in training large language models (LLMs)…

Yupeng Chang, Xu Wang, Jindong Wang, et al.

 

SCAIS Framework Paper

Artificial intelligence systems (AI systems) are complex socio-technical-ecological systems…

S.M Towhidul Islam Tonmoy, S M Mehedi Za

 


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