Search Results for author:
Found 36 papers, 13 papers with code
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations
This work emphasizes the need for targeted approaches to gender and semantic coherence in machine translation, particularly for genderless languages, contributing to more equitable and accurate translation systems.
Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition
Knowledge distillation allows smaller neural networks to emulate the performance of larger, teacher models with reduced computational demands.
TALE: A Tool-Augmented Framework for Reference-Free Evaluation of Large Language Models
TALE enhances the reliability of LLM evaluations in real-world, dynamic scenarios without relying on static references.
Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through Books
Our findings indicate that LLMs trained on decade-specific books manifest biases reflective of their times, with both gradual trends and notable shifts.
Think or Step-by-Step? UnZIPping the Black Box in Zero-Shot Prompts
For instance, while both 'step-by-step' and 'think' show high ZIP scores, which one is more influential depends on the model and task.
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index
As prompt engineering research rapidly evolves, evaluations beyond accuracy are crucial for developing cost-effective techniques.
NYT-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers
Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable.
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing.
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models
This study explores the effectiveness of Large Language Models (LLMs) in creating personalized "mirror stories" that reflect and resonate with individual readers' identities, addressing the significant lack of diversity in literature.
Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge
Utilizing GPT-4 for prompt generation and Diffusion Attentive Attribution Maps (DAAM) for heatmap analysis, we propose a novel evaluation framework that isolates the models' ability in pronoun disambiguation from other visual processing challenges.
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models
Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models' internal and expressed confidence.
Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models
Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models' outputs toward particular social narratives.
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries
Our results emphasize the challenge posed by EvoGrad: Even the best performing LLM, GPT-3. 5, achieves an accuracy of 65. 0% with an average error depth of 7. 2, a stark contrast to human performance of 92.
WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts
The Winograd Schema Challenge (WSC) serves as a prominent benchmark for evaluating machine understanding.
Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models
Debiasing methods that seek to mitigate the tendency of Language Models (LMs) to occasionally output toxic or inappropriate text have recently gained traction.
Dynamic-Pix2Pix: Noise Injected cGAN for Modeling Input and Target Domain Joint Distributions with Limited Training Data
Helping the cGAN learn the target distribution from noise input results in a better model generalization during the test time and allows the model to fit almost perfectly to the target domain distribution.
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance.
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring
We identify a practical cost for our method and model: high GPU-time for natural language evaluation.
Not-so fine-tuning: Measures of Common Sense for Language Models
Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding.
ADEPT: An Adjective-Dependent Plausibility Task
A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors.
An Analysis of Dataset Overlap on Winograd-Style Tasks
The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning
Reduction of the number of channels could reduce the complexity of brain-computer-interface devices.
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales
Intracranial tumors are groups of cells that usually grow uncontrollably.
Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging.
Gland Segmentation in Histopathological Images by Deep Neural Network
Histology method is vital in the diagnosis and prognosis of cancers and many other diseases.
Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age.
Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features
Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease.
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG.
Ranked #36 on
Coreference Resolution
on Winograd Schema Challenge
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged.
A Knowledge Hunting Framework for Common Sense Reasoning
We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge.
Ranked #65 on
Coreference Resolution
on Winograd Schema Challenge
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images
The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network.
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA).
Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference
Deep neural networks have shown great achievements in solving complex problems.
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
By increasing the volume of telemedicine information, the need for medical image compression has become more important.
Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks
Recent advances in capsule endoscopy systems have introduced new methods and capabilities.
