Vinh Q. Tran
I’m a Staff Research Engineer at Google Research NY where I work methods for sequence modeling / NLP and machine learning. My research interests are in all things related to improving, expanding, or rethinking the functionality of Transformers and other state-of-the-art sequence models.
Research Areas
Authored Publications
Sort By
UL2: Unifying Language Learning Paradigms
Yi Tay
Xavier Garcia
Jason Wei
Hyung Won Chung
Steven Zheng
Denny Zhou
Neil Houlsby
ICLR (2023)
Preview abstract
Existing pre-trained models are generally geared towards a particular class of
problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for
pre-training models that are universally effective across datasets and setups. We
begin by disentangling architectural archetypes with pre-training objectives – two
concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training
objectives can be cast as one another and how interpolating between different
objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pretraining objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning
is associated with specific pre-training schemes. We conduct extensive ablative
experiments to compare multiple pre-training objectives and find that our method
pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across
multiple diverse setups. Finally, by scaling our model up to 20B parameters, we
achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language
understanding, text classification, question answering, commonsense reasoning,
long text reasoning, structured knowledge grounding and information retrieval.
Our model also achieve strong results at in-context learning, outperforming 175B
GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on oneshot summarization. Finally, we show that UL2 20B works well with chain-ofthought prompting and reasoning tasks, making it an appealing choice for research
into reasoning at a small to medium scale of 20B parameters. We publicly release
Flax-based T5X model checkpoints for the 20B model.
View details
DSI++: Updating Transformer Memory with New Documents
Yi Tay
Jinfeng Rao
Emma Strubell
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Preview abstract
Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents.
Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting significantly. Concretely, it improves the average Hits@10 by +21.1% over competitive baselines for NQ and requires 6 times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.
View details
Understanding Generative Retrieval at Scale
Ronak Pradeep
Jimmy Lin
EMNLP 2023
Preview abstract
Popularized by the Differentiable Search Index, the emerging paradigm of Generative Retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus into the parameters of a single transformer. Although many different approaches have been proposed to improve the effectiveness of generative retrieval, they have only been evaluated on document corpora on the order of 100k in size. We conduct the first study of generative retrieval techniques across various corpus scales, ultimately scaling up to the entire MS MARCO passage ranking task consisting of 8.8M passages. After ablating for the most promising techniques, we then consider model scales up to 11B parameters. Along the way, we uncover several findings about scaling generative retrieval to millions of passages. Notably, the use of synthetic query generation as document representation is the only modeling technique critical to retrieval effectiveness. In addition, we find that the strongest performing architecture modifications from the literature at T5-Base initialization only perform well due to added parameters. Naively scaling to a comparable model size outperforms these proposed techniques. Finally, while model scale is necessary as corpus size increases, we find that given existing techniques, scaling model parameters past a certain point can be detrimental for retrieval effectiveness. This result might be counter-intuitive to the commonly held belief that model capacity is a limiting factor for scaling generative retrieval to larger corpora, and suggests the need for more fundamental improvements. In general, we believe that these findings will be highly valuable for the community to clarify the state of generative retrieval at scale and highlight the challenges currently facing the paradigm.
View details
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification
Yi Tay
Chaitanya Kamath
Shailesh Bavadekar
Evgeniy Gabrilovich
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (2022)
Preview abstract
With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision.
View details
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers
Alyssa Whitlock Lees
Yi Tay
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)
Preview abstract
On the world wide web, toxic content detectors are a crucial line ofdefense against potentially hateful and offensive messages. As such,building highly effective classifiers that enable a safer internet is animportant research area. Moreover, the web is a highly multilingual,cross-cultural community that develops its own lingo over time.As such, developing models that can be effective across a diverserange of languages usages and styles is crucial. In this paper, wepresent Jigsaw Perspective API’s new generation of toxic contentclassifiers which takes a step towards this unified vision. At theheart of the approach is a single multilingual token-free Charformermodel that is applicable across languages, domains, and tasks. Wedemonstrate that by forgoing static vocabularies, we gain flexibilityacross a variety of settings. We additionally outline the techniquesemployed to make such a byte-level model efficient and feasible forproductionization. Through extensive experiments on multilingualtoxic comment classification benchmarks derived from real API traffic and evaluation on an array of code-switching, covert toxicity,emoji-based hate, human-readable obfuscation, distribution shift,and bias evaluation settings, we show that our proposed approachoutperforms strong baselines. Finally, we present our findings ofdeploying this system in production, and discuss our observedbenefits over traditional approaches
View details
Transformer Memory as a Differentiable Search Index
Yi Tay
Jianmo Ni
Harsh Mehta
Zhe Zhao
NeurIPS 2022
Preview abstract
In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifying the whole retrieval process. We study variations in how documents and their identifiers are represented, variations in training procedures, and the interplay between models and corpus sizes. Experiments demonstrate that given appropriate design choices, DSI significantly outperforms strong baselines such as dual encoder models. Moreover, DSI demonstrates strong generalization capabilities, outperforming a BM25 baseline in a zero-shot setup.
View details
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Pat Verga
Jianmo Ni
arXiv (2022)
Preview abstract
Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in this setting. We propose and study Attributed QA as a key first step in the development of attributed LLMs. We develop a reproducable evaluation framework for the task, using human annotations as a gold standard and a correlated automatic metric that we show is suitable for development settings. We describe and benchmark a broad set of architectures for the task. Our contributions give some concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third key question (How to build LLMs with attribution?).
View details
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Vamsi Aribandi
Yi Tay
Jinfeng Rao
Steven Zheng
Jianmo Ni
Sebastian Ruder
ICLR 2022
Preview abstract
Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training. Towards this goal, this paper introduces ExMix (Extreme Mixture): a massive collection of 107 supervised NLP tasks across diverse domains and task-families. Using ExMix, we study the effect of multi-task pre-training at the largest scale to date, and analyze co-training transfer amongst common families of tasks. Through this analysis, we show that manually curating an ideal set of tasks for multi-task pre-training is not straightforward, and that multi-task scaling can vastly improve models on its own. Finally, we propose ExT5: a model pre-trained using a multi-task objective of self-supervised span denoising and supervised ExMix. Via extensive experiments, we show that ExT5 outperforms strong T5 baselines on SuperGLUE, GEM, Rainbow, Closed-Book QA tasks, and several tasks outside of ExMix. ExT5 also significantly improves sample efficiency while pre-training.
View details
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
Yi Tay
Sebastian Ruder
Hyung Won Chung
Cong Yu
ICLR (2022)
Preview abstract
State-of-the-art models in natural language processing rely on separate rigid subword tokenization algorithms, which limit their generalization ability and adaptation to new settings. In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. To this end, we introduce a soft gradient-based subword tokenization module (GBST) that automatically learns latent subword representations from characters in a data-driven fashion. Concretely, GBST enumerates candidate subword blocks and learns to score them in a position-wise fashion using a block scoring network. We additionally introduce Charformer, a deep Transformer model that integrates GBST and operates on the byte level. Via extensive experiments on English GLUE, multilingual, and noisy text datasets, we show that Charformer outperforms a series of competitive byte-level baselines while generally performing on par and sometimes outperforming subword-based models. Additionally, Charformer is fast, improving the speed of both vanilla byte-level and subword-level Transformers by 28%-100% while maintaining competitive quality. We believe this work paves the way for highly performant token-free models that are trained completely end-to-end.
View details
Confident Adaptive Language Modeling
Adam Fisch
Yi Tay
NeurIPS 2022
Preview abstract
Recent advances in Transformer-based large language models (LLMs) achieved significant performance improvements across many tasks.
These gains come with a drastic increase in the models' size, leading to slow and costly use at inference time.
In practice, however, the series of generations made by LLMs is composed of varying levels of difficulty. While certain predictions truly benefit from the models' full capacity, other continuations are more trivial and can be solved with reduced compute.
In this work, we introduce Confident Adaptive Language Modeling (CALM), a method for dynamically allocating different amounts of compute per example and per generation timestep.
Early exit decoding involves several challenges that we address here, such as: (1) what confidence measure to use; (2) connecting sequence-level constraints to local per-token exit decisions; and (3) attending back to missing hidden representations due to early exits in previous tokens.
Through theoretical analysis and empirical experiments on three diverse generation tasks, we demonstrate the efficacy of our method in reliably reducing compute while maintaining high performance.
View details
