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Found 33 papers, 12 papers with code
Uncovering Surprising Event Boundaries in Narratives
It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research.
How to be Helpful on Online Support Forums?
Internet forums such as Reddit offer people a platform to ask for advice when they encounter various issues at work, school or in relationships.
Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRiskDilemmas
Detecting AI risks becomes more challenging as stronger models emerge and find novel methods such as Alignment Faking to circumvent these detection attempts.
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.
SEE: Continual Fine-tuning with Sequential Ensemble of Experts
Some approaches use routers to assign tasks to experts, but in continual learning, they often require retraining for optimal performance.
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.
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing
Dynamical systems theory provides a framework for analyzing iterative processes and evolution over time.
From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning
Scaling data and model size has been proven effective for boosting the performance of large language models.
Diverging Preferences: When do Annotators Disagree and do Models Know?
We find that the majority of disagreements are in opposition with standard reward modeling approaches, which are designed with the assumption that annotator disagreement is noise.
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.
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models
The development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize external information.
Data, Data Everywhere: A Guide for Pretraining Dataset Construction
The impressive capabilities of recent language models can be largely attributed to the multi-trillion token pretraining datasets that they are trained on.
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.
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text.
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.
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.
Can We Trust the Similarity Measurement in Federated Learning?
We first uncover the deficiencies of similarity metrics that high-dimensional local models, including benign and poisoned models, may be evaluated to have the same similarity while being significantly different in the parameter values.
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF
Model alignment with human preferences is an essential step in making Large Language Models (LLMs) helpful and consistent with human values.
Humanoid Agents: Platform for Simulating Human-like Generative Agents
Just as computational simulations of atoms, molecules and cells have shaped the way we study the sciences, true-to-life simulations of human-like agents can be valuable tools for studying human behavior.
MAGE: Machine-generated Text Detection in the Wild
In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources.
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering
The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer.
Transformer-Empowered Content-Aware Collaborative Filtering
Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to enrich both item and user representations.
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning
Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed.
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost.
A Systematic Survey of Blockchained Federated Learning
Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL.
Extracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences
While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness.
Author2Vec: A Framework for Generating User Embedding
Online forums and social media platforms provide noisy but valuable data every day.
NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining
In this paper, we propose a novel end-to-end Neuron Attention Stage-by-Stage Net (NASNet), which can solve all types of rain model tasks efficiently.
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels.
Ranked #1 on
Image Dehazing
on KITTI
No, you're not alone: A better way to find people with similar experiences on Reddit
This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts.
