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- Carnegie Mellon UniversityUS49 papers
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8 papers · 2 filters
On Early Detection of Hallucinations in Factual Question Answering
Ben Snyder, Marius Moisescu, Muhammad Bilal Zafar
While large language models (LLMs) have taken great strides towards helping humans with a plethora of tasks, hallucinations remain a major impediment towards gaining user trust. Th…
Efficient Continual Pre-training for Building Domain Specific Large Language Models
Yong Xie, Karan Aggarwal, Aitzaz Ahmad
Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling…
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting
Chao-Han Huck Yang, Yile Gu, Yi-Chieh Liu +3
We explore the ability of large language models (LLMs) to act as speech recognition post-processors that perform rescoring and error correction. Our first focus is on instruction p…
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
Yu Yu, Chao-Han Huck Yang, Jari Kolehmainen +15
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have s…
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding
Zi Yang, Samridhi Choudhary, Siegfried Kunzmann +1
Fine-tuned transformer models have shown superior performances in many natural language tasks. However, the large model size prohibits deploying high-performance transformer models…
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation
Anaelia Ovalle, Palash Goyal, Jwala Dhamala +5
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. Given the recent popularity and adoption of language gener…