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Eyes Can Deceive: Benchmarking Counterfactual Reasoning Abilities of Multi-modal Large Language Models
Counterfactual reasoning, as a crucial manifestation of human intelligence, refers to making presuppositions based on established facts and extrapolating potential outcomes.
Pre-training Universal Language Representation
Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units.
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment
We present a universal representation model, BURT (BERT-inspired Universal Representation from learning meaningful segmenT), to encode different levels of linguistic unit into the same vector space.
Learning Universal Representations from Word to Sentence
Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way.
BURT: BERT-inspired Universal Representation from Twin Structure
Pre-trained contextualized language models such as BERT have shown great effectiveness in a wide range of downstream Natural Language Processing (NLP) tasks.
