Search Results for author:
Found 21 papers, 9 papers with code
Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing
We present a system for bilingual Data-ToText Generation and Semantic Parsing.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Generative Models for 3D Point Clouds
Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space.
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
One common solution to this is augmenting LLMs with a retrieval system and making sure that the generated output is attributable to the retrieved information.
Transcending Scaling Laws with 0.1% Extra Compute
This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute.
Ranked #2 on
Cross-Lingual Question Answering
on TyDiQA-GoldP
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts
The resulting soft knowledge prompts (KPs) are task independent and work as an external memory of the LMs.
Reducing Retraining by Recycling Parameter-Efficient Prompts
Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model behavior when concatenated to the input text.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences.
UL2: Unifying Language Learning Paradigms
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 one-shot summarization.
Ranked #7 on
Long-range modeling
on SCROLLS
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models
Pre-trained encoder-decoder transformer architectures have become increasingly popular recently with the advent of T5 models.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets.
Towards Zero-Shot Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension
We propose a simple method to generate multilingual question and answer pairs on a large scale through the use of a single generative model.
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions.
Embedding-based Zero-shot Retrieval through Query Generation
In some cases, our model trained on synthetic data can even outperform the same model trained on real data
TableQnA: Answering List Intent Queries With Web Tables
For (ii), we develop novel features to compute structure-aware match and train a machine learning model.
Open Domain Question Answering Using Web Tables
Tables extracted from web documents can be used to directly answer many web search queries.
Label Dependent Deep Variational Paraphrase Generation
Generating paraphrases that are lexically similar but semantically different is a challenging task.
Knowledge Distillation in Document Retrieval
In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings.
Differentiable Greedy Networks
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization.
