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Found 8 papers, 2 papers with code
Towards Inference-time Category-wise Safety Steering for Large Language Models
While large language models (LLMs) have seen unprecedented advancements in capabilities and applications across a variety of use-cases, safety alignment of these models is still an area of active research.
Unsupervised Extraction of Dialogue Policies from Conversations
Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts
As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase.
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
Recent advancements in instruction-tuning datasets have predominantly focused on specific tasks like mathematical or logical reasoning.
NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue
We show that canonical forms offer a promising alternative to traditional methods for intent classification.
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
Ranked #10 on
Zero-Shot Learning
on MedConceptsQA
