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Found 23 papers, 10 papers with code
Axiomatic Preference Modeling for Longform Question Answering
The remarkable abilities of large language models (LLMs) like GPT-4 partially stem from post-training processes like Reinforcement Learning from Human Feedback (RLHF) involving human preferences encoded in a reward model.
Orca 2: Teaching Small Language Models How to Reason
Research on training small LMs has often relied on imitation learning to replicate the output of more capable models.
Ranked #1 on
Crass AI
on BIG-bench
Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation
Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind.
Hybrid Retrieval-Augmented Generation for Real-time Composition Assistance
Retrieval augmentation enhances performance of traditional language models by incorporating additional context.
FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts
Our gating function harnesses the knowledge of a pretrained model common expert to enhance its routing decisions on-the-fly.
Boosting Natural Language Generation from Instructions with Meta-Learning
Recent work has shown that language models (LMs) trained with multi-task \textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved performance compared to prompt tuning.
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice.
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels
In this work, we propose a method to leverage weak/noisy labels (e. g., risk scores generated by machine rules for detecting malware) that are cheaper to obtain for anomaly detection.
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning
Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from.
Knowledge Infused Decoding
Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks.
Ranked #2 on
Question Answering
on KILT: ELI5
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks.
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion
We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously.
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces
In this paper, we aim to the address of the problem of few shot task learning by exploiting and transferring from a different task which admits a related but disparate label space.
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU.
A Dataset and Baselines for Multilingual Reply Suggestion
Reply suggestion models help users process emails and chats faster.
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach.
Self-Training with Weak Supervision
In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task.
Learning with Weak Supervision for Email Intent Detection
In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
Meta Label Correction for Noisy Label Learning
We view the label correction procedure as a meta-process and propose a new meta-learning based framework termed MLC (Meta Label Correction) for learning with noisy labels.
Ranked #9 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data
In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure.
Asymmetric Variational Autoencoders
However, freely enriching the family of variational distribution is challenging since the ELBO requires variational likelihood evaluations of the latent variables.
Convolutional Normalizing Flows
Variational inference provides one way to approximate the posterior distribution, however its expressive power is limited and so is the accuracy of resulting approximation.
