27 citations · 102 across the 13 of their papers we have counts for
18 papers · 1 filter
See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses
Yulong Chen, Yang Liu, Jianhao Yan +6
The impressive performance of Large Language Models (LLMs) has consistently surpassed numerous human-designed benchmarks, presenting new challenges in assessing the shortcomings of…
Investigating Data Contamination for Pre-training Language Models
Minhao Jiang, Ken Ziyu Liu, Ming Zhong +4
Language models pre-trained on web-scale corpora demonstrate impressive capabilities on diverse downstream tasks. However, there is increasing concern whether such capabilities mig…
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Yizhu Jiao, Ming Zhong, Sha Li +4
Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural…
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu +7
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…
Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective
Ming Zhong, Chenxin An, Weizhu Chen +2
Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operati…
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Yulong Chen, Huajian Zhang, Yijie Zhou +8
Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can conta…