Search Results for author:
Found 11 papers, 5 papers with code
Using Item Response Theory to Measure Gender and Racial Bias of a BERT-based Automated English Speech Assessment System
This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment.
White Men Lead, Black Women Help: Uncovering Gender, Racial, and Intersectional Bias in Language Agency
Social biases can manifest in language agency.
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
In this survey, we review prior studies on dimensions of bias: Gender, Skintone, and Geo-Culture.
The Male CEO and the Female Assistant: Probing Gender Biases in Text-To-Image Models Through Paired Stereotype Test
Recent large-scale Text-To-Image (T2I) models such as DALLE-3 demonstrate great potential in new applications, but also face unprecedented fairness challenges.
Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
We explore the correlation between the level of hallucination in model responses and two types of sequence-level certainty: probabilistic certainty and semantic certainty.
"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters
Through benchmarking evaluation on 2 popular LLMs- ChatGPT and Alpaca, we reveal significant gender biases in LLM-generated recommendation letters.
Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems
Recent advancements in Large Language Models empower them to follow freeform instructions, including imitating generic or specific demographic personas in conversations.
PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation
Existing fine-tuning methods for this task are costly as all the parameters of the model need to be updated during the training process.
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression
To begin with, we are among the first to comprehensively investigate mainstream KD techniques on DNS models to resolve the two challenges.
TheoremQA: A Theorem-driven Question Answering dataset
We evaluate a wide spectrum of 16 large language and code models with different prompting strategies like Chain-of-Thoughts and Program-of-Thoughts.
Ranked #1 on
Natural Questions
on TheoremQA
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models.
