publications

2024

1. AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

   Zhaowei Wang, Haochen Shi, Weiqi Wang, and 5 more authors

   In Findings of the Association for Computational Linguistics: NAACL 2024, Jun 2024

   Abs

   Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present AbsPyramid, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, AbsPyramid collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks.

2. AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation

   Zhaowei Wang, Wei Fan, Qing Zong, and 7 more authors

   In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Aug 2024

   Abs

   Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs’ abstraction ability through instruction tuning. The framework builds instructions with in-depth explanations to assist LLMs in capturing the underlying rationale of abstraction. Meanwhile, we introduce a plausibility estimator to select instructions that are more consistent with the abstraction knowledge of LLMs to be aligned. Then, our framework combines abstraction instructions with general-purpose ones to build a hybrid dataset. Extensive experiments and analyses demonstrate that our framework can considerably enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

3. Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers

   Sehyun Choi

   Aug 2024

2023

1. EMNLP 2023

   KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection

   Sehyun Choi, Tianqing Fang, Zhaowei Wang, and 1 more author

   EMNLP 2023, Oct 2023

   arXiv Bib Code Poster Slides

   @article{choi2023kcts,
     title = {KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection},
     author = {Choi, Sehyun and Fang, Tianqing and Wang, Zhaowei and Song, Yangqiu},
     year = {2023},
     month = oct,
     journal = {EMNLP 2023},
   }

2. arXiv

   CKBP v2: An Expert-Annotated Evaluation Set for Commonsense Knowledge Base Population

   Tianqing Fang, Quyet V. Do, Sehyun Choi, and 2 more authors

   arXiv preprint, Apr 2023

   arXiv Bib Code

   @article{fang2023ckbp,
     journal = {arXiv preprint},
     title = {CKBP v2: An Expert-Annotated Evaluation Set for Commonsense Knowledge Base Population},
     author = {Fang, Tianqing and Do, Quyet V. and Choi, Sehyun and Wang, Weiqi and Song, Yangqiu},
     year = {2023},
     month = apr,
     eprint = {2304.10392},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
   }

2021

1. Fine-grained Pneumonia Detection from Chest X-Rays Using Complementary Model Training

   Duhyeuk Chang, Sungbin Lee, and Sehyun Choi

   한국정보과학회 학술발표논문집, Jun 2021

   Bib PDF

   @article{chang_lee_choi_2021,
     title = {Fine-grained Pneumonia Detection from Chest X-Rays Using Complementary Model Training},
     journal = {한국정보과학회 학술발표논문집},
     author = {Chang, Duhyeuk and Lee, Sungbin and Choi, Sehyun},
     year = {2021},
     month = jun,
     pages = {1004–1006},
   }

2. EMNLP 2021

   Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset

   Tianqing Fang, Weiqi Wang, Sehyun Choi, and 4 more authors

   EMNLP 2021, Sep 2021

   Bib PDF Code

   @article{fang_choi_2021,
     title = {Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset},
     journal = {EMNLP 2021},
     author = {Fang, Tianqing and Wang, Weiqi and Choi, Sehyun and Hao, Shibo and Zhang, Hongming and Song, Yangqiu and He, Bin},
     year = {2021},
     month = sep,
   }