no code implementations • ICML 2020 • Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, Jian Tang
This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation.
1 code implementation • 11 Oct 2023 • Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, Danqi Chen
As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models.
1 code implementation • 10 Oct 2023 • Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, Danqi Chen
In this work, we study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models.
Ranked #25 on
Question Answering
on PIQA
1 code implementation • NeurIPS 2023 • Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory.
1 code implementation • 24 May 2023 • Tianyu Gao, Howard Yen, Jiatong Yu, Danqi Chen
We propose ALCE, the first benchmark for Automatic LLMs' Citation Evaluation.
1 code implementation • 16 May 2023 • Jane Pan, Tianyu Gao, Howard Chen, Danqi Chen
Large language models (LLMs) exploit in-context learning (ICL) to solve tasks with only a few demonstrations, but its mechanisms are not yet well-understood.
no code implementations • 25 Apr 2023 • Lei Shi, Tianyu Gao, Zheng Zhang, Junxing Zhang
Deep learning based models for medical image segmentation have made great progress in recent years.
no code implementations • 10 Nov 2022 • Leonard Adolphs, Tianyu Gao, Jing Xu, Kurt Shuster, Sainbayar Sukhbaatar, Jason Weston
Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples.
no code implementations • 15 Oct 2022 • Ruisheng Ran, Tianyu Gao, Bin Fang
Recently, Transformer is much popular and plays an important role in the fields of Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV), etc.
1 code implementation • COLING 2022 • Zichun Yu, Tianyu Gao, Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie zhou
Prompting, which casts downstream applications as language modeling tasks, has shown to be sample efficient compared to standard fine-tuning with pre-trained models.
1 code implementation • 17 May 2022 • Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, Danqi Chen
For the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences.
1 code implementation • 16 Feb 2022 • Alexander Wettig, Tianyu Gao, Zexuan Zhong, Danqi Chen
In this work, we revisit this important choice of MLM pre-training.
2 code implementations • ACL 2022 • Huihan Li, Tianyu Gao, Manan Goenka, Danqi Chen
In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers.
1 code implementation • Findings (ACL) 2021 • Tianyu Gao, Xu Han, Keyue Qiu, Yuzhuo Bai, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie zhou
Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data.
20 code implementations • EMNLP 2021 • Tianyu Gao, Xingcheng Yao, Danqi Chen
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
Ranked #3 on
Linear-Probe Classification
on SentEval
9 code implementations • ACL 2021 • Tianyu Gao, Adam Fisch, Danqi Chen
We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples.
Ranked #6 on
Zero-Shot Text Classification
on AG News
1 code implementation • 8 Dec 2020 • Yuexin Wu, Tianyu Gao, Sihao Wang, Zhongmin Xiong
As the first attempt in this field to address this problem, we propose a flexible dual-optimizer model to gain robustness from both regression loss and classification loss.
1 code implementation • COLING 2020 • Bowen Dong, Yuan YAO, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun
Few-shot classification requires classifiers to adapt to new classes with only a few training instances.
1 code implementation • EMNLP 2020 • Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie zhou
We find that (i) while context is the main source to support the predictions, RE models also heavily rely on the information from entity mentions, most of which is type information, and (ii) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks.
Ranked #23 on
Relation Extraction
on TACRED
1 code implementation • 5 Jul 2020 • Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux, Jian Tang
To more effectively generalize to new relations, in this paper we study the relationships between different relations and propose to leverage a global relation graph.
no code implementations • ACL 2020 • Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie zhou
Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie zhou
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text.
1 code implementation • 13 Nov 2019 • Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text.
1 code implementation • IJCNLP 2019 • Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, Jie zhou
We present FewRel 2. 0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances?
1 code implementation • IJCNLP 2019 • Xu Han, Tianyu Gao, Yuan YAO, Demin Ye, Zhiyuan Liu, Maosong Sun
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE).
1 code implementation • 29 Aug 2019 • Tianyu Gao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun
To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations.