1 citations · 3 across the 20 of their papers we have counts for
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Lightweight Safety Guardrails Using Fine-tuned BERT Embeddings
Aaron Zheng, Mansi Rana, Andreas Stolcke
With the recent proliferation of large language models (LLMs), enterprises have been able to rapidly develop proof-of-concepts and prototypes. As a result, there is a growing need…
Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso +7
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong AS…
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output
Hithesh Sankararaman, Mohammed Nasheed Yasin, Tanner Sorensen +2
We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG). Given a context and putative output, we compute a factuality score th…
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models
Ambuje Gupta, Mrinal Rawat, Andreas Stolcke +1
Retrieval augmented generation (RAG) pipelines are commonly used in tasks such as question-answering (QA), relying on retrieving relevant documents from a vector store computed usi…