14 citations · 31 across the 10 of their papers we have counts for
10 papers
SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL
Ke Shen, Mayank Kejriwal
In recent years,Text-to-SQL, the problem of automatically converting questions posed in natural language to formal SQL queries, has emerged as an important problem at the intersect…
Defining and Evaluating Decision and Composite Risk in Language Models Applied to Natural Language Inference
Ke Shen, Mayank Kejriwal
Despite their impressive performance, large language models (LLMs) such as ChatGPT are known to pose important risks. One such set of risks arises from misplaced confidence, whethe…
An Evaluation of Estimative Uncertainty in Large Language Models
Zhisheng Tang, Ke Shen, Mayank Kejriwal
Words of estimative probability (WEPs), such as ''maybe'' or ''probably not'' are ubiquitous in natural language for communicating estimative uncertainty, compared with direct stat…
A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores
Ke Shen, Mayank Kejriwal
Large Language Models (LLMs), such as ChatGPT, have achieved impressive milestones in natural language processing (NLP). Despite their impressive performance, the models are known…
Understanding Substructures in Commonsense Relations in ConceptNet
Ke Shen, Mayank Kejriwal
Acquiring commonsense knowledge and reasoning is an important goal in modern NLP research. Despite much progress, there is still a lack of understanding (especially at scale) of th…
Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes
Ke Shen, Mayank Kejriwal
Recent work on transformer-based neural networks has led to impressive advances on multiple-choice natural language understanding (NLU) problems, such as Question Answering (QA) an…
Can Scale-free Network Growth with Triad Formation Capture Simplicial Complex Distributions in Real Communication Networks?
Mayank Kejriwal, Ke Shen
In recent years, there has been a growing recognition that higher-order structures are important features in real-world networks. A particular class of structures that has gained p…
A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
Henrique Santos, Ke Shen, Alice M. Mulvehill +3
Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community. Current CSR benchmarks use multiple-choice (an…
Do Fine-tuned Commonsense Language Models Really Generalize?
Mayank Kejriwal, Ke Shen
Recently, transformer-based methods such as RoBERTa and GPT-3 have led to significant experimental advances in natural language processing tasks such as question answering and comm…
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base
Ke Shen, Mayank Kejriwal
Acquiring commonsense knowledge and reasoning is recognized as an important frontier in achieving general Artificial Intelligence (AI). Recent research in the Natural Language Proc…