Search Results for author:
Found 9 papers, 3 papers with code
Style Transfer: From Stitching to Neural Networks
This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background.
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge.
DHP Benchmark: Are LLMs Good NLG Evaluators?
Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks.
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking
API call generation is the cornerstone of large language models' tool-using ability that provides access to the larger world.
Co$^2$PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Pre-trained Language Models are widely used in many important real-world applications.
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model
Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems.
Faithful Low-Resource Data-to-Text Generation through Cycle Training
Cycle training uses two models which are inverses of each other: one that generates text from structured data, and one which generates the structured data from natural language text.
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features.
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge
We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
