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Found 18 papers, 5 papers with code
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages
To address this need, we introduce HelpSteer3-Preference, a permissively licensed (CC-BY-4. 0), high-quality, human-annotated preference dataset comprising of over 40, 000 samples.
Llama-Nemotron: Efficient Reasoning Models
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use.
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
We introduce Nemotron-H, a family of 8B and 56B/47B hybrid Mamba-Transformer models designed to reduce inference cost for a given accuracy level.
HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Tasks
To this end, we collect HelpSteer3 data to train dedicated Feedback and Edit Models that are capable of performing inference-time scaling for open-ended general-domain tasks.
HelpSteer2-Preference: Complementing Ratings with Preferences
Reward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style.
LLM Pruning and Distillation in Practice: The Minitron Approach
We present a comprehensive report on compressing the Llama 3. 1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation.
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG) have become popular methods for adapting large language models while minimizing compute requirements.
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.
HelpSteer2: Open-source dataset for training top-performing reward models
Using a powerful internal base model trained on HelpSteer2, we are able to achieve the SOTA score (92. 0%) on Reward-Bench's primary dataset, outperforming currently listed open and proprietary models, as of June 12th, 2024.
NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment
Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe.
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.
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
To alleviate this problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated for the various aspects that make responses helpful.
Uncovering Main Causalities for Long-tailed Information Extraction
Information Extraction (IE) aims to extract structural information from unstructured texts.
Adversarial Learning with Mask Reconstruction for Text-Guided Image Inpainting
In this paper, we propose an adversarial learning framework with mask reconstruction (ALMR) for image inpainting with textual guidance, which consists of a two-stage generator and dual discriminators.
Counterfactual Thinking for Long-tailed Information Extraction
However, in practice, the long-tailed and imbalanced data may lead to severe bias issues for deep learning models, due to very few training instances available for the tail classes.
Contrastive Self-supervised Learning for Graph Classification
A contrastive loss is defined to learn graph encoders by judging whether two augmented graphs are from the same original graph.
MedDialog: Two Large-scale Medical Dialogue Datasets
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
