Search Results for author:
Found 13 papers, 7 papers with code
CodeS: Towards Building Open-source Language Models for Text-to-SQL
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
However, existing ICL approaches to ER typically necessitate providing a task description and a set of demonstrations for each entity pair and thus have limitations on the monetary cost of interfacing LLMs.
SEED: Domain-Specific Data Curation With Large Language Models
SEED uses these generated modules to process most of the data records and dynamically decides when the LLM should step in to directly process some individual records, possibly using the data-access modules to retrieve relevant information from the data sources to assist the LLM in solving the task.
VerifAI: Verified Generative AI
We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI.
Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
The widely used practice is to build task-specific or even dataset-specific solutions, which are hard to generalize and disable the opportunities of knowledge sharing that can be learned from different datasets and multiple tasks.
ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions
Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming.
Contextual Expressive Text-to-Speech
To achieve this task, we construct a synthetic dataset and develop an effective framework.
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4. 7 points (85. 6% vs. 80. 9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1. 5 points (90. 6% vs. 92. 1%).
Ranked #2 on
Table-based Fact Verification
on TabFact
Domain Adaptation for Deep Entity Resolution: A Design Space Exploration
Entity resolution (ER) is a core problem of data integration.
Ranked #2 on
Entity Resolution
on WDC Watches-small
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
RPT is pre-trained for a tuple-to-tuple model by corrupting the input tuple and then learning a model to reconstruct the original tuple.
Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches.
Crowd-Powered Data Mining
Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification.
