Papers by Jason Wei
Language Model Augmented Relevance Score (2021.acl-long)
Copied to clipboard
| Challenge: | Existing metrics that compare the candidate with the human reference do not consider the context, resulting in poor correlation with human judgements. |
| Approach: | They propose a language model-aware metric that augments the human reference while considering the context to provide evaluation scores that correlate highly with human judgements. |
| Outcome: | The proposed metric achieves higher correlation with human reference judgements and differentiates well-formed candidates from adversarial samples to a larger degree. |
Good-Enough Example Extrapolation (2021.emnlp-main)
Copied to clipboard
| Challenge: | GE3 is a data augmentation protocol that can be used to increase text examples from one class onto another. |
| Approach: | They propose a data augmentation protocol that extrapolates the hidden space distribution of text examples from one class onto another to investigate whether this bias is valid for data augmented. |
| Outcome: | The proposed protocol improves on three text classification datasets for various data imbalance scenarios. |
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)
Copied to clipboard
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. |
Text Augmentation in a Multi-Task View (2021.eacl-main)
Copied to clipboard
| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Inverse Scaling Can Become U-Shaped (2023.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Linguistic Complexity Loss in Text-Based Therapy (2021.naacl-main)
Copied to clipboard
| Challenge: | linguistic complexity loss in text-based therapy can be used to identify patterns of mental health . authors: clients who reported more anxiety used less lexically diverse language . |
| Approach: | They analyze linguistic complexity loss in online therapy conversations as it relates to mental health . they find that clients used less lexically diverse language when they were more anxious . |
| Outcome: | The proposed analysis shows that therapists use more complex language when clients are anxious . the authors show that analyzing linguistic complexity can identify meaningful patterns in mental health . |
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. |
| Approach: | They propose to fine tune financial text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs including both public and proprietary financial documents. |
| Outcome: | The proposed embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embeddING from OpenAI. |
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (D19-1)
Copied to clipboard
| Challenge: | Existing data augmentation techniques for text classification are difficult to implement and cost a high amount of money. |
| Approach: | They propose to use four simple but powerful operations to boost performance on text classification tasks to improve synonym replacement, random insertion, random swap, and random deletion. |
| Outcome: | The proposed techniques improve performance on five classification tasks and are particularly useful for smaller datasets. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
Copied to clipboard
Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
Frequency Effects on Syntactic Rule Learning in Transformers (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules. |
| Approach: | They investigate the performance of BERT on English subject–verb agreement by analyzing word frequency and absolute frequency of verb forms. |
| Outcome: | The proposed model generalizes well to subject–verb pairs that never occurred in training, suggesting a degree of rule-governed behavior. |
A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity (2024.naacl-long)
Copied to clipboard
Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, Daphne Ippolito
| Challenge: | a large number of pretraining data design practices are under-documented, authors say . authors: strong performance of modern language models depends on selfsupervised pretraining . |
| Approach: | They propose to pretrain models on data curated at different collection times . they find temporal shift between evaluation data and pretraining data leads to performance degradation . |
| Outcome: | The results validate, quantify, and expose many undocumented intuitions about text pretraining . authors say this practice has outperformed other models in the field . |
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2023.findings-acl)
Copied to clipboard
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. |
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)
Copied to clipboard
Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot
| Challenge: | EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions. |
| Approach: | They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment. |
| Outcome: | Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction. |
Modulating Language Models with Emotions (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for generating context-aware language that embodies diverse emotions are dull or generic due to limited training data for diverse emotions. |
| Approach: | They propose a modulated layer normalization technique that generates emotional responses using large pre-trained models. |
| Outcome: | The proposed method outperforms baseline methods on the MojiTalk dataset while maintaining diversity, fluency, and coherence. |
A Recipe for Arbitrary Text Style Transfer with Large Language Models (2022.acl-short)
Copied to clipboard
| Challenge: | augmented zero-shot learning is a prompting method that allows large language models to perform zero-shoot text style transfer to arbitrary styles, without any model fine-tuning or exemplars in the target style. |
| Approach: | They propose a prompting method that frames style transfer as a sentence rewriting task and requires only a natural language instruction. |
| Outcome: | The proposed method is based on a large language model and is shown to perform on standard style transfer tasks and arbitrary transformations. |
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (2021.naacl-main)
Copied to clipboard
| Challenge: | a few-shot text classification task requires a large number of output classes, with few training examples per class. |
| Approach: | They propose a data augmentation technique suitable for training with limited data for few-shot, highly-multiclass text classification scenarios. |
| Outcome: | The proposed technique improves performance on four classification tasks by 3.0% on average. |
A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
Copied to clipboard
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
| Approach: | They present a comprehensive survey of data augmentation for NLP by summarizing the literature in a structured manner. |
| Outcome: | The proposed methods are used for popular NLP applications and tasks and highlight current challenges and directions for future research. |
A Cognitive Regularizer for Language Modeling (2021.acl-long)
Copied to clipboard
| Challenge: | a uniform information density hypothesis is used to explain certain linguistic phenomena . a regularizer that encodes the UID hypothesis can be used for language training . |
| Approach: | They propose to augment the canonical MLE objective with a regularizer that encodes UID . they find that regularization consistently improves perplexity in language models . |
| Outcome: | The proposed hypothesis can be operationalized as an inductive bias for language modeling. |