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Found 18 papers, 6 papers with code
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems.
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
However, even the top-performing 13B LLM-based translation models, like ALMA, does not match the performance of state-of-the-art conventional encoder-decoder translation models or larger-scale LLMs such as GPT-4.
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
In this study, we propose a novel fine-tuning approach for LLMs that is specifically designed for the translation task, eliminating the need for the abundant parallel data that traditional translation models usually depend on.
Leveraging GPT-4 for Automatic Translation Post-Editing
In this work, we formalize the task of direct translation post-editing with Large Language Models (LLMs) and explore the use of GPT-4 to automatically post-edit NMT outputs across several language pairs.
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
In this paper, we present a comprehensive evaluation of GPT models for machine translation, covering various aspects such as quality of different GPT models in comparison with state-of-the-art research and commercial systems, effect of prompting strategies, robustness towards domain shifts and document-level translation.
On Hard Episodes in Meta-Learning
To address the issue of sub-par performance on hard episodes, we investigate and benchmark different meta-training strategies based on adversarial training and curriculum learning.
A Flexible Measurement of Diversity in Datasets with Random Network Distillation
We validate and deploy this metric on both images and text.
Semi-Supervised Few-Shot Intent Classification and Slot Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems.
Data Augmentation for Meta-Learning
Conventional image classifiers are trained by randomly sampling mini-batches of images.
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
We validate and deploy this metric on both images and text.
Active Imitation Learning with Noisy Guidance
Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies.
Meta-Learning for Few-Shot NMT Adaptation
We frame the adaptation of NMT systems as a meta-learning problem, where we learn to adapt to new unseen domains based on simulated offline meta-training domain adaptation tasks.
Learning Effective Exploration Strategies For Contextual Bandits
We develop a meta-learning algorithm, MELEE, that learns an exploration policy based on simulated, synthetic contextual bandit tasks.
Meta-Learning for Contextual Bandit Exploration
We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting.
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
In the cross-lingual setup, we assume there is access to annotated resources as well as a well trained model in the source language and little to no annotated data in the target language.
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode.
Structured Prediction via Learning to Search under Bandit Feedback
We present an algorithm for structured prediction under online bandit feedback.
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task.
