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Found 30 papers, 9 papers with code
Rethinking Sentiment Style Transfer
To overcome this issue, we propose a graph-based method to extract attribute content and attribute-independent content from input sentences in the YELP dataset and IMDB dataset.
Diverse Preference Optimization
In this work we introduce Diverse Preference Optimization (DivPO), an optimization method which learns to generate much more diverse responses than standard pipelines, while maintaining the quality of the generations.
R.I.P.: Better Models by Survival of the Fittest Prompts
Using Llama 3. 1-8B-Instruct, RIP improves AlpacaEval2 LC Win Rate by 9. 4%, Arena-Hard by 8. 7%, and WildBench by 9. 9%.
Adaptive Decoding via Latent Preference Optimization
During language model decoding, it is known that using higher temperature sampling gives more creative responses, while lower temperatures are more factually accurate.
Self-Taught Evaluators
Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation.
Distilling System 2 into System 1
Large language models (LLMs) can spend extra compute during inference to generate intermediate thoughts, which helps to produce better final responses.
Following Length Constraints in Instructions
Aligned instruction following models can better fulfill user requests than their unaligned counterparts.
TCMD: A Traditional Chinese Medicine QA Dataset for Evaluating Large Language Models
Moreover, we also analyze the robustness of current LLMs in solving TCM QA tasks by introducing randomness.
Efficient Tool Use with Chain-of-Abstraction Reasoning
LLM agents trained with our method also show more efficient tool use, with inference speed being on average ~1. 4x faster than baseline tool-augmented LLMs.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
The ART of LLM Refinement: Ask, Refine, and Trust
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations?
Ranked #54 on
Arithmetic Reasoning
on GSM8K
Self-Alignment with Instruction Backtranslation
We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions.
Shepherd: A Critic for Language Model Generation
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs.
OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models
Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations.
LIMA: Less Is More for Alignment
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences.
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world.
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.
Ranked #26 on
Natural Language Inference
on RTE
ALERT: Adapting Language Models to Reasoning Tasks
Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning.
STT: Soft Template Tuning for Few-Shot Adaptation
Significantly, it can even outperform the time- and resource-consuming fine-tuning method on sentiment classification tasks.
Efficient Language Modeling with Sparse all-MLP
All-MLP architectures have attracted increasing interest as an alternative to attention-based models.
Ranked #17 on
Question Answering
on StoryCloze
SDA: Improving Text Generation with Self Data Augmentation
Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.
ReMP: Rectified Metric Propagation for Few-Shot Learning
Few-shot learning features the capability of generalizing from a few examples.
Structure-Aware Human-Action Generation
Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.
Ranked #2 on
Human action generation
on NTU RGB+D 2D
Bayesian Meta Sampling for Fast Uncertainty Adaptation
Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck
Variational autoencoders (VAEs) are essential tools in end-to-end representation learning.
Feature Quantization Improves GAN Training
The instability in GAN training has been a long-standing problem despite remarkable research efforts.
Ranked #1 on
Image-to-Image Translation
on anime-to-selfie
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.
Ranked #4 on
Human action generation
on NTU RGB+D 2D
Generating Adversarial Examples With Conditional Generative Adversarial Net
Recently, deep neural networks have significant progress and successful application in various fields, but they are found vulnerable to attack instances, e. g., adversarial examples.
TensorFlow.js: Machine Learning for the Web and Beyond
TensorFlow. js is a library for building and executing machine learning algorithms in JavaScript.
