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9 papers · 1 filter
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition
Anna Seo Gyeong Choi, Jonghyeon Park, Myungwoo Oh
Recent advancements in machine learning have significantly improved speech recognition, but recognizing speech from non-fluent or accented speakers remains a challenge. Previous ef…
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks
Varsha Suresh, Salah Aït-Mokhtar, Caroline Brun +1
Self-supervised learning models have revolutionized the field of speech processing. However, the process of fine-tuning these models on downstream tasks requires substantial comput…
A Survey on Large Language Models for Code Generation
Juyong Jiang, Fan Wang, Jiasi Shen +2
Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code…
SLM as Guardian: Pioneering AI Safety with Small Language Models
Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi +6
Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of humans. However, internalizing s…
Weakly Supervised Pre-Training for Multi-Hop Retriever
Yeon Seonwoo, Sang-Woo Lee, Ji-Hoon Kim +2
In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-question de…
Self-Guided Contrastive Learning for BERT Sentence Representations
Taeuk Kim, Kang Min Yoo, Sang-goo Lee
Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we…