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Found 14 papers, 7 papers with code
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications.
Extreme Miscalibration and the Illusion of Adversarial Robustness
Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify.
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
Large Language Models (LLMs) are powerful zero-shot assessors and are increasingly used in real-world situations such as for written exams or benchmarking systems.
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT.
Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems
Minimum Bayes' Risk (MBR) decoding can be used to combine system outputs in a manner that encourages better alignment with the final assessment criterion.
Sample Attackability in Natural Language Adversarial Attacks
Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches.
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting.
Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
In this work, adversarial attacks for NMT systems are explored from an output perception perspective.
Rewarding Chatbots for Real-World Engagement with Millions of Users
The proposed approach uses automatic pseudo-labels collected from user interactions to train a reward model that can be used to reject low-scoring sample responses generated by the chatbot model at inference time.
Identifying Adversarially Attackable and Robust Samples
We propose a deep-learning-based detector to identify the adversarially attackable and robust samples in an unseen dataset for an unseen target model.
L2 proficiency assessment using self-supervised speech representations
Though the wav2vec 2. 0 based system is found to be sensitive to the nature of the response, it can be configured to yield comparable performance to systems requiring a speech transcription, and yields gains when appropriately combined with standard approaches.
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment
When considering the application of GEC systems to automated language assessment, the aim of an adversary could be to cheat by making a small change to a grammatically incorrect input sentence that conceals the errors from a GEC system, such that no edits are found and the candidate is unjustly awarded a perfect fluency score.
Residue-Based Natural Language Adversarial Attack Detection
Many popular image adversarial detection approaches are able to identify adversarial examples from embedding feature spaces, whilst in the NLP domain existing state of the art detection approaches solely focus on input text features, without consideration of model embedding spaces.
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
However, many tasks of practical interest have different modalities, such as tabular data, audio, text, or sensor data, which offer significant challenges involving regression and discrete or continuous structured prediction.
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