26 citations · 96 across the 17 of their papers we have counts for
7 papers · 1 filter
Contextualized Scene Imagination for Generative Commonsense Reasoning
PeiFeng Wang, Jonathan Zamora, Junfeng Liu +3
Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) s…
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,…
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models
Woojeong Jin, Yu Cheng, Yelong Shen +2
Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning. However, these VL models are hard to…
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
Donghan Yu, Chenguang Zhu, Yuwei Fang +6
Current Open-Domain Question Answering (ODQA) model paradigm often contains a retrieving module and a reading module. Given an input question, the reading module predicts the answe…
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia +5
Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and Commonsen…
Discretized Integrated Gradients for Explaining Language Models
Soumya Sanyal, Xiang Ren
As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation. It…