31 citations · 135 across the 20 of their papers we have counts for
8 papers · 1 filter
SCOTT: Self-Consistent Chain-of-Thought Distillation
Peifeng Wang, Zhengyang Wang, Zheng Li +3
Large language models (LMs) beyond a certain scale, demonstrate the emergent capability of generating free-text rationales for their predictions via chain-of-thought (CoT) promptin…
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Peifeng Wang, Aaron Chan, Filip Ilievski +2
Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To…
Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Information
Zhipeng Zhang, Xinglin Hou, Kai Niu +5
Recently, online shopping has gradually become a common way of shopping for people all over the world. Wonderful merchandise advertisements often attract more people to buy. These…
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…
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
Changlong Yu, Jialong Han, Peifeng Wang +4
We address hypernymy detection, i.e., whether an is-a relationship exists between words (x, y), with the help of large textual corpora. Most conventional approaches to this task ha…
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Mrigank Raman, Aaron Chan, Siddhant Agarwal +7
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation. By using attention over…
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
Peifeng Wang, Nanyun Peng, Filip Ilievski +2
Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to obtain th…
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
Yanlin Feng, Xinyue Chen, Bill Yuchen Lin +3
Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transp…