Papers by Kejian Shi
On Learning to Summarize with Large Language Models as References (2024.naacl-long)
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Yixin Liu, Kejian Shi, Katherine He, Longtian Ye, Alexander Fabbri, Pengfei Liu, Dragomir Radev, Arman Cohan
| Challenge: | Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators when compared to reference summary from widely used summarization datasets. |
| Approach: | They propose to use large language models (LLMs) as reference learning settings for smaller text summarization models to investigate whether their performance can be substantially improved. |
| Outcome: | The proposed model outperforms standard supervised fine-tuning and human evaluations while retaining human-level performance. |
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding (2023.findings-emnlp)
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| Challenge: | Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs. |
| Approach: | They propose a unlikelihood loss that encourages generation of simpler terms and a reranked beam search decoding method that optimizes for simplicity. |
| Outcome: | The proposed methods achieve better performance on readability metrics on three datasets. |
Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)
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| Challenge: | Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions . |
| Approach: | They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. |
| Outcome: | The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation. |
ReIFE: Re-evaluating Instruction-Following Evaluation (2025.naacl-long)
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Yixin Liu, Kejian Shi, Alexander Fabbri, Yilun Zhao, PeiFeng Wang, Chien-Sheng Wu, Shafiq Joty, Arman Cohan
| Challenge: | Existing evaluations of large language models (LLMs) for instruction following are incomplete. |
| Approach: | They propose to use 25 base LLMs and 15 recently proposed evaluation protocols to evaluate instruction following on 4 human-annotated datasets. |
| Outcome: | The proposed evaluations identify the best-performing base LLMs and evaluation protocols with a high degree of robustness. |
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature (2025.emnlp-main)
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David Wadden, Kejian Shi, Jacob Morrison, Alan Li, Aakanksha Naik, Shruti Singh, Nitzan Barzilay, Kyle Lo, Tom Hope, Luca Soldaini, Shannon Zejiang Shen, Doug Downey, Hannaneh Hajishirzi, Arman Cohan
| Challenge: | ScIRIFF is the only entirely expert-written instruction-following dataset for scientific literature understanding . it features complex instructions with long input contexts, detailed task descriptions, and structured outputs. |
| Approach: | They present a dataset of 137K instruction-following instances for training and evaluation . they finetuned large language models using a mix of general domain and ScIRIFF instructions . |
| Outcome: | The proposed dataset shows that on nine out-of-distribution held-out tasks, the model performs better than baselines trained on general domain instructions. |