Papers by Yisong Su

2 papers
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models (2023.findings-emnlp)

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Challenge: Large language models (LLMs) have shown nearly saturated performance on many NLP tasks.
Approach: They construct multiple sensitive factors time QA which encompasses three temporal factors . they test current mainstream LLMs with different parameter sizes .
Outcome: The proposed model incorporates three temporal factors with 2,853 samples . the results show that LLMs fall behind smaller models on these factors .
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)

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Challenge: Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue.
Approach: They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions.
Outcome: The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses.

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