1.1k citations · 2.1k across the 63 of their papers we have counts for
31 papers · 1 filter
SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
Yung-Sung Chuang, Benjamin Cohen-Wang, Shannon Zejiang Shen +6
We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated resp…
Text Quality-Based Pruning for Efficient Training of Language Models
Vasu Sharma, Karthik Padthe, Newsha Ardalani +8
In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this pa…
Chameleon: Mixed-Modal Early-Fusion Foundation Models
Chameleon Team
We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable…
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma +8
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowled…
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering
Chyi-Jiunn Lin, Guan-Ting Lin, Yung-Sung Chuang +5
Spoken Question Answering (SQA) is essential for machines to reply to user's question by finding the answer span within a given spoken passage. SQA has been previously achieved wit…
SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERT
Cheol Jun Cho, Abdelrahman Mohamed, Shang-Wen Li +2
Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing. Yet, the discovered units often remain in phonetic s…
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
Seamless Communication, Loïc Barrault, Yu-An Chung +65
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed…
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
Yung-Sung Chuang, Wei Fang, Shang-Wen Li +2
We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. EAR first applies a query expansio…
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target
Guan-Wei Wu, Guan-Ting Lin, Shang-Wen Li +1
Spoken Language Understanding (SLU) is a task that aims to extract semantic information from spoken utterances. Previous research has made progress in end-to-end SLU by using paire…
Introducing Semantics into Speech Encoders
Derek Xu, Shuyan Dong, Changhan Wang +10
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recogni…
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
Tzu-hsun Feng, Annie Dong, Ching-Feng Yeh +11
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency. The challenge buil…
Meta Learning for Natural Language Processing: A Survey
Hung-yi Lee, Shang-Wen Li, Ngoc Thang Vu
Deep learning has been the mainstream technique in natural language processing (NLP) area. However, the techniques require many labeled data and are less generalizable across domai…
Self-Supervised Speech Representation Learning: A Review
Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt +9
Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenari…
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo +7
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between…
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang +14
Transfer learning has proven to be crucial in advancing the state of speech and natural language processing research in recent years. In speech, a model pre-trained by self-supervi…
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Andy T. Liu, Wei Xiao, Henghui Zhu +3
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label ef…
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
Guan-Ting Lin, Yung-Sung Chuang, Ho-Lam Chung +7
Spoken Question Answering (SQA) is to find the answer from a spoken document given a question, which is crucial for personal assistants when replying to the queries from the users.…
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu +5
Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world,…
Pairwise Supervised Contrastive Learning of Sentence Representations
Dejiao Zhang, Shang-Wen Li, Wei Xiao +4
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese los…
Mitigating Biases in Toxic Language Detection through Invariant Rationalization
Yung-Sung Chuang, Mingye Gao, Hongyin Luo +4
Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attribute…
Meta-learning for downstream aware and agnostic pretraining
Hongyin Luo, Shuyan Dong, Yung-Sung Chuang +1
Neural network pretraining is gaining attention due to its outstanding performance in natural language processing applications. However, pretraining usually leverages predefined ta…
SUPERB: Speech processing Universal PERformance Benchmark
Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang +17
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large…
Cooperative Self-training of Machine Reading Comprehension
Hongyin Luo, Shang-Wen Li, Mingye Gao +2
Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-qualit…
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
Hongyin Luo, Shang-Wen Li, James Glass
In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clin…