Papers by Jingting Ye

3 papers
RealBehavior: A Framework for Faithfully Characterizing Foundation Models’ Human-like Behavior Mechanisms (2023.findings-emnlp)

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Challenge: Existing studies on human-like behaviors in foundation models do not verify their faithfulness . a simple application of psychological tools cannot faithfully characterize all human-type behaviors .
Approach: They propose a framework to characterize humanoid behaviors in foundation models . they argue that a simple application of psychological tools cannot faithfully characterize all human-like behaviors .
Outcome: The proposed framework assesses the faithfulness of results based on reproducibility, internal consistency, and generalizability.
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization (2023.findings-emnlp)

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Challenge: Large language models (LLMs) have implicitly transfer knowledge across languages, but not all languages have such generalization capabilities.
Approach: They propose a meta-learning-based method to learn to align conceptual spaces of different languages to enhance cross-lingual generalization.
Outcome: The proposed method achieves competitive results with state-of-the-art methods and narrows the performance gap between languages.
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? (2022.emnlp-main)

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Challenge: Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages.
Approach: They propose to use multilingual BERT to enable zero-shot cross-lingual transfer of syntactic knowledge between different languages by generating grammatical relations in 24 different languages.
Outcome: The results show that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.

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