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Found 10 papers, 2 papers with code
Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI
In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e. g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e. g., will my partner be able to tell that this is me?).
AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents
We design a set of realistic scenarios where language agents are instructed to achieve goals that are in conflict with being truthful during a multi-turn conversation with simulated human agents.
Program-Aided Reasoners (better) Know What They Know
Our analysis uncovers that prompting styles that produce lesser diversity in generations also have more calibrated results, and thus we also experiment with inducing lower generation diversity using temperature scaling and find that for certain temperatures, PAL is not only more accurate but is also more calibrated than COT.
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks.
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model
Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills.
Multi-lingual and Multi-cultural Figurative Language Understanding
Figurative language permeates human communication, but at the same time is relatively understudied in NLP.
Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language
The second compares the proposed work's scores with those of the baseline models and offers a rise in performance by 6%.
Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations
Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalised, efficient and dynamic recommendation systems.
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance
Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem.
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems
This number is increasing further due to COVID-19 and the associated automation of education and testing.
