1.2k citations · 1.4k across the 8 of their papers we have counts for
6 papers · 1 filter
Tailoring Self-Rationalizers with Multi-Reward Distillation
Sahana Ramnath, Brihi Joshi, Skyler Hallinan +6
Large language models (LMs) are capable of generating free-text rationales to aid question answering. However, prior work 1) suggests that useful self-rationalization is emergent o…
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
Liunian Harold Li, Jack Hessel, Youngjae Yu +3
Chain-of-thought prompting (e.g., "Let's think step-by-step") primes large language models to verbalize rationalization for their predictions. While chain-of-thought can lead to dr…
DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation
Jingnong Qu, Liunian Harold Li, Jieyu Zhao +2
Disinformation has become a serious problem on social media. In particular, given their short format, visual attraction, and humorous nature, memes have a significant advantage in…
On the Paradox of Learning to Reason from Data
Honghua Zhang, Liunian Harold Li, Tao Meng +2
Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to an…
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions
Liunian Harold Li, Haoxuan You, Zhecan Wang +3
Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks. However, existing models require a large amount of parallel imag…
Efficient Contextual Representation Learning Without Softmax Layer
Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh +1
Contextual representation models have achieved great success in improving various downstream tasks. However, these language-model-based encoders are difficult to train due to the l…