Papers by Quoc Le
Semi-Supervised Sequence Modeling with Cross-View Training (D18-1)
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| Challenge: | Unsupervised representation learning algorithms such as word2vec and ELMo only learn from task-specific labeled data during the main training phase. |
| Approach: | They propose a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
| Outcome: | The proposed algorithm improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
Symbol tuning improves in-context learning in language models (2023.emnlp-main)
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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le
| Challenge: | Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. |
| Approach: | They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols. |
| Outcome: | The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model. |
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)
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Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong
| Challenge: | Modern large language models often "hallucinate" plausible but factually incorrect information, which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical. |
| Approach: | They develop a human evaluation procedure to measure correctness and hallucination and use it to benchmark both closed and open-source LLMs. |
| Outcome: | The proposed method outperforms both competing search engine-augmented prompting methods and commercial systems on search-augmented QA. |
Inverse Scaling Can Become U-Shaped (2023.emnlp-main)
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| Challenge: | Scaling up language models has been shown to improve performance on a wide range of downstream tasks, but are there any tasks for which performance gets worse as models scale? |
| Approach: | They evaluate models trained on five times more compute and evaluated them on 280B parameters and 500 zettaFLOPs of training compute. |
| Outcome: | The proposed tasks show that performance decreases as models scale and increases again as models get larger. |
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning (2021.emnlp-main)
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| Challenge: | Recent advances in NLP demonstrate the effectiveness of applying large-scale pre-trained language models to downstream tasks. |
| Approach: | They propose a method that uses task augmentation to fine-tune unlabeled data. |
| Outcome: | The proposed approach improves sample efficiency across 12 few-shot benchmarks. |
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2023.findings-acl)
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Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, Jason Wei
| Challenge: | Language models have already made good progress on this benchmark, with the best model outperforming average reported human-rater results on 65% of the BIG-Bench tasks. |
| Approach: | They propose to use chain-of-thought prompting to challenge language models on 23 challenging BIG-Bench tasks which they call BIG-Bench Hard. |
| Outcome: | The proposed language models outperform the average human-rater on 65% of the BIG-Bench tasks. |
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context (P19-1)
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| Challenge: | Term memory networks (RNNs) are difficult to optimize due to gradient vanishing and explosion. |
| Approach: | They propose a neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. |
| Outcome: | The proposed method improves state-of-the-art performance on short and long sequences and generates coherent, novel text articles with thousands of tokens. |
Pre-Training Transformers as Energy-Based Cloze Models (2020.emnlp-main)
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| Challenge: | elucidates close connection between cloze modeling and representation learning over text. |
| Approach: | They propose an energy-based cloze model for representation learning over text . they assign a scalar energy score to each input token indicating how likely it is given context . |
| Outcome: | The proposed model performs better than masked language models and faster than cloze models. |
AirDialogue: An Environment for Goal-Oriented Dialogue Research (D18-1)
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| Challenge: | Recent advances in dialogue generation have inspired a number of studies on dialogue systems . however, current datasets are limited in size and the environment for training agents is relatively unsophisticated. |
| Approach: | They propose to use a context-generator to generate travel and flight restrictions to train agents. |
| Outcome: | The proposed model achieves a score of 0.17 while humans can reach 0.91 . the proposed model is based on a large dataset that contains 301,427 goal-oriented conversations . |