Bill Yuchen Lin

LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation

2020 · arXiv (Cornell University)

Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from and collecting the requisite number of labels involves massive human effort. In …

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

2025 · arXiv (Cornell University)

We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance on logic …

Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media

2017

In this paper, we present our multichannel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 …

Neural Adaptation Layers for Cross-domain Named Entity Recognition

2018

Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training …

KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning

2019

Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge

2021

Question: I have five fingers but I am not alive. What am I? Answer: a glove