Search Results for author:
Found 9 papers, 6 papers with code
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference
A core ingredient in RLHF's success in aligning and improving large language models (LLMs) is its reward model, trained using human feedback on model outputs.
An Empirical Study of Translation Hypothesis Ensembling with Large Language Models
Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output.
Non-Exchangeable Conformal Risk Control
Split conformal prediction has recently sparked great interest due to its ability to provide formally guaranteed uncertainty sets or intervals for predictions made by black-box neural models, ensuring a predefined probability of containing the actual ground truth.
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data.
Quality-Aware Decoding for Neural Machine Translation
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.
Sparse Communication via Mixed Distributions
Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols.
Sparse Continuous Distributions and Fenchel-Young Losses
In contrast, for finite domains, recent work on sparse alternatives to softmax (e. g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support.
Multimodal Continuous Visual Attention Mechanisms
Visual attention mechanisms are a key component of neural network models for computer vision.
Sparse and Continuous Attention Mechanisms
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e. g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation).
Ranked #36 on
Visual Question Answering (VQA)
on VQA v2 test-std
