6 papers · 1 filter
Reranking Laws for Language Generation: A Communication-Theoretic Perspective
António Farinhas, Haau-Sing Li, André F. T. Martins
To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers. A simple and often used strategy is to…
QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
Gonçalo R. A. Faria, Sweta Agrawal, António Farinhas +3
An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correl…
Can Automatic Metrics Assess High-Quality Translations?
Sweta Agrawal, António Farinhas, Ricardo Rei +1
Automatic metrics for evaluating translation quality are typically validated by measuring how well they correlate with human assessments. However, correlation methods tend to captu…
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?
Josef Jon
This thesis argues that the currently widely used Natural Language Processing algorithms possibly have various limitations related to the properties of the texts they handle and pr…
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference
Miguel Moura Ramos, Patrick Fernandes, António Farinhas +1
Reinforcement learning from human feedback (RLHF) is a recent technique to improve the quality of the text generated by a language model, making it closer to what humans would gene…
Conformal Prediction for Natural Language Processing: A Survey
Margarida M. Campos, António Farinhas, Chrysoula Zerva +2
The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as…