31 citations · 35 across the 5 of their papers we have counts for
6 papers · 1 filter
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
Chenglei Si, Dan Friedman, Nitish Joshi +3
In-context learning (ICL) is an important paradigm for adapting large language models (LLMs) to new tasks, but the generalization behavior of ICL remains poorly understood. We inve…
Getting MoRE out of Mixture of Language Model Reasoning Experts
Chenglei Si, Weijia Shi, Chen Zhao +2
While recent large language models (LLMs) improve on various question answering (QA) datasets, it remains difficult for a single model to generalize across question types that requ…
What's in a Name? Answer Equivalence For Open-Domain Question Answering
Chenglei Si, Chen Zhao, Jordan Boyd-Graber
A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are consid…
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning
Chenglei Si, Zhengyan Zhang, Fanchao Qi +4
Pretrained language models (PLMs) perform poorly under adversarial attacks. To improve the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted to co…
CharBERT: Character-aware Pre-trained Language Model
Wentao Ma, Yiming Cui, Chenglei Si +3
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almos…
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
Chenglei Si, Shuohang Wang, Min-Yen Kan +1
Multiple-Choice Reading Comprehension (MCRC) requires the model to read the passage and question, and select the correct answer among the given options. Recent state-of-the-art mod…