126 citations · 388 across the 24 of their papers we have counts for
7 papers · 1 filter
Credal Transformer: A Principled Approach for Quantifying and Mitigating Hallucinations in Large Language Models
Shihao Ji, Zihui Song, Jiajie Huang
Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems from the Transformer's Softmax function, which creates "Artif…
Data-Driven Adaptive Simultaneous Machine Translation
Guangxu Xun, Mingbo Ma, Yuchen Bian +7
In simultaneous translation (SimulMT), the most widely used strategy is the wait-k policy thanks to its simplicity and effectiveness in balancing translation quality and latency. H…
Better than BERT but Worse than Baseline
Boxiang Liu, Jiaji Huang, Xingyu Cai +1
This paper compares BERT-SQuAD and Ab3P on the Abbreviation Definition Identification (ADI) task. ADI inputs a text and outputs short forms (abbreviations/acronyms) and long forms…
Language Modeling at Scale
Mostofa Patwary, Milind Chabbi, Heewoo Jun +3
We show how Zipf's Law can be used to scale up language modeling (LM) to take advantage of more training data and more GPUs. LM plays a key role in many important natural language…
Large Margin Neural Language Model
Jiaji Huang, Yi Li, Wei Ping +1
We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences.…
Reducing Bias in Production Speech Models
Eric Battenberg, Rewon Child, Adam Coates +13
Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and…
Active Learning for Speech Recognition: the Power of Gradients
Jiaji Huang, Rewon Child, Vinay Rao +3
In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples…