[PDF][PDF] Metrics for what, metrics for whom: assessing actionability of bias evaluation metrics in NLP
This paper introduces the concept of actionability in the context of bias measures in natural
language processing (NLP). We define actionability as the degree to which a …
language processing (NLP). We define actionability as the degree to which a …
Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
Following on recent advances in large language models (LLMs) and subsequent chat
models, a new wave of large vision-language models (LVLMs) has emerged. Such models …
models, a new wave of large vision-language models (LVLMs) has emerged. Such models …
Are Large Language Models Rational Investors?
Large Language Models (LLMs) are progressively being adopted in financial analysis to
harness their extensive knowledge base for interpreting complex market data and trends …
harness their extensive knowledge base for interpreting complex market data and trends …
I Think, Therefore I am: Awareness in Large Language Models
Do large language models (LLMs) exhibit any forms of awareness similar to humans? In this
paper, we introduce the concept of awareness to LLMs, arguing that awareness is an …
paper, we introduce the concept of awareness to LLMs, arguing that awareness is an …
A taxonomy of stereotype content in large language models
This study introduces a taxonomy of stereotype content in contemporary large language
models (LLMs). We prompt ChatGPT 3.5, Llama 3, and Mixtral 8x7B, three powerful and …
models (LLMs). We prompt ChatGPT 3.5, Llama 3, and Mixtral 8x7B, three powerful and …
Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions
Recent advancements in general-purpose AI have highlighted the importance of guiding AI
systems towards the intended goals, ethical principles, and values of individuals and …
systems towards the intended goals, ethical principles, and values of individuals and …
Profiling Bias in LLMs: Stereotype dimensions in Contextual word embeddings
Large language models (LLMs) are the foundation of the current successes of artificial
intelligence (AI), however, they are unavoidably biased. To effectively communicate the risks …
intelligence (AI), however, they are unavoidably biased. To effectively communicate the risks …
LLMs are Vulnerable to Malicious Prompts Disguised as Scientific Language
As large language models (LLMs) have been deployed in various real-world settings,
concerns about the harm they may propagate have grown. Various jailbreaking techniques …
concerns about the harm they may propagate have grown. Various jailbreaking techniques …
Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes
Examining the alignment of large language models (LLMs) has become increasingly
important, particularly when these systems fail to operate as intended. This study explores …
important, particularly when these systems fail to operate as intended. This study explores …
GeniL: A Multilingual Dataset on Generalizing Language
LLMs are increasingly transforming our digital ecosystem, but they often inherit societal
biases learned from their training data, for instance stereotypes associating certain attributes …
biases learned from their training data, for instance stereotypes associating certain attributes …