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Found 8 papers, 5 papers with code
Limits for Learning with Language Models
With the advent of large language models (LLMs), the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks.
Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering
While transformer models achieve high performance on standard question answering tasks, we show that they fail to be semantically faithful once we perform these interventions for a significant number of cases (~50% for deletion intervention, and ~20% drop in accuracy for negation intervention).
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection
We specifically aim to attack the widely used Faster R-CNN by changing the predicted label for a particular object in an image: where prior work has targeted one specific object (a stop sign), we generalise to arbitrary objects, with the key challenge being the need to change the labels of all bounding boxes for all instances of that object type.
Exploring the Robustness of NMT Systems to Nonsensical Inputs
In this paper, we study the behaviour of NMT systems when multiple changes are made to the source sentence.
Mimic and Fool: A Task Agnostic Adversarial Attack
Keeping this in mind, we propose Mimic and Fool, a task agnostic adversarial attack.
CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images
This paper presents an approach for automatic detection of Munro's Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis.
CNN for Text-Based Multiple Choice Question Answering
The task of Question Answering is at the very core of machine comprehension.
A Neural Lemmatizer for Bengali
Given a word along with its contextual neighbours as input, the model is designed to produce the lemma of the concerned word as output.
