← Search

Dixuan Wang

3 accepted papers

2025

Benchmarking Language Model Creativity: A Case Study on Code Generation

NAACL 2025long

As LLMs become increasingly prevalent, it is interesting to consider how “creative” these models can be. From cognitive science, creativity consists of at least two key characteristics: convergent thinking (purposefulness to achieve a given goal) and divergent thinking (adaptability to explore new e…

2024

Reason from Fallacy: Enhancing Large Language Models’ Logical Reasoning through Logical Fallacy Understanding

NAACL 2024findings

Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning. One non-negligible reason for LLMs’ suboptimal performance on logical reasoning is their overlooking of understanding lo…

Cited by 7SourcePDFScholar
2024

ToNER: Type-oriented Named Entity Recognition with Generative Language Model

COLING 2024main

In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has also been found that the information related to entities, such as entity types, can prompt a model to achieve NER bette…