2.4k citations · 12.3k across the 115 of their papers we have counts for
16 papers · 1 filter
-CoT: Prolog-Initialized Chain-of-Thought Prompting for Multi-Hop Question-Answering
Chao Wan, Albert Gong, Mihir Mishra +3
Chain-of-Thought (CoT) prompting significantly enhances large language models' (LLMs) problem-solving capabilities, but still struggles with complex multi-hop questions, often fall…
Pre-training Limited Memory Language Models with Internal and External Knowledge
Linxi Zhao, Sofian Zalouk, Christian K. Belardi +7
Neural language models are black-boxes--both linguistic patterns and factual knowledge are distributed across billions of opaque parameters. This entangled encoding makes it diffic…
Diffusion Guided Language Modeling
Justin Lovelace, Varsha Kishore, Yiwei Chen +1
Current language models demonstrate remarkable proficiency in text generation. However, for many applications it is desirable to control attributes, such as sentiment, or toxicity,…
Correction with Backtracking Reduces Hallucination in Summarization
Zhenzhen Liu, Chao Wan, Varsha Kishore +3
Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neu…
On the Effectiveness of Offline RL for Dialogue Response Generation
Paloma Sodhi, Felix Wu, Ethan R. Elenberg +2
A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different w…
Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction
Boyi Li, Rodolfo Corona, Karttikeya Mangalam +7
Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on…
Latent Diffusion for Language Generation
Justin Lovelace, Varsha Kishore, Chao Wan +2
Diffusion models have achieved great success in modeling continuous data modalities such as images, audio, and video, but have seen limited use in discrete domains such as language…
Long-term Control for Dialogue Generation: Methods and Evaluation
Ramya Ramakrishnan, Hashan Buddhika Narangodage, Mauro Schilman +2
Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constraine…
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Felix Wu, Kwangyoun Kim, Shinji Watanabe +4
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete repres…
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
Felix Wu, Kwangyoun Kim, Jing Pan +3
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture…
Revisiting Few-sample BERT Fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar +2
This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cau…
Integrated Triaging for Fast Reading Comprehension
Felix Wu, Boyi Li, Lequn Wang +3
Although according to several benchmarks automatic machine reading comprehension (MRC) systems have recently reached super-human performance, less attention has been paid to their…
BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu +2
We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sente…
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
Felix Wu, Boyi Li, Lequn Wang +3
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12]. FusionNet is a high performing reading comprehension architecture, which was designed…
Fast Reading Comprehension with ConvNets
Felix Wu, Ni Lao, John Blitzer +2
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelizat…
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
Xilun Chen, Yu Sun, Ben Athiwaratkun +2
In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most lang…