Should chatgpt be biased? challenges and risks of bias in large language models

E Ferrara - arxiv preprint arxiv:2304.03738, 2023 - arxiv.org
As the capabilities of generative language models continue to advance, the implications of
biases ingrained within these models have garnered increasing attention from researchers …

Trustllm: Trustworthiness in large language models

Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li… - arxiv preprint arxiv …, 2024 - arxiv.org
Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

[HTML][HTML] The butterfly effect in artificial intelligence systems: Implications for AI bias and fairness

E Ferrara - Machine Learning with Applications, 2024 - Elsevier
The concept of the Butterfly Effect, derived from chaos theory, highlights how seemingly
minor changes can lead to significant, unpredictable outcomes in complex systems. This …

[HTML][HTML] Position: TrustLLM: Trustworthiness in large language models

Y Huang, L Sun, H Wang, S Wu… - International …, 2024 - proceedings.mlr.press
Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …

Foundational challenges in assuring alignment and safety of large language models

U Anwar, A Saparov, J Rando, D Paleka… - arxiv preprint arxiv …, 2024 - arxiv.org
This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Striking the balance in using LLMs for fact-checking: A narrative literature review

L Dierickx, A Van Dalen, AL Opdahl… - … on Disinformation in Open …, 2024 - Springer
The launch of ChatGPT at the end of November 2022 triggered a general reflection on its
benefits for supporting fact-checking workflows and practices. Between the excitement of the …

Expertqa: Expert-curated questions and attributed answers

C Malaviya, S Lee, S Chen, E Sieber, M Yatskar… - arxiv preprint arxiv …, 2023 - arxiv.org
As language models are adapted by a more sophisticated and diverse set of users, the
importance of guaranteeing that they provide factually correct information supported by …

Factcheck-bench: Fine-grained evaluation benchmark for automatic fact-checkers

Y Wang, RG Reddy, Z Mujahid, A Arora… - Findings of the …, 2024 - aclanthology.org
The increased use of large language models (LLMs) across a variety of real-world
applications calls for mechanisms to verify the factual accuracy of their outputs. In this work …

[PDF][PDF] Enhancing contextual understanding of mistral llm with external knowledge bases

M Sasaki, N Watanabe, T Komanaka - 2024 - assets-eu.researchsquare.com
This study explores the enhancement of contextual understanding and factual accuracy in
Language Learning Models (LLMs), specifically Mistral LLM, through the integration of …

Factcheck-GPT: End-to-End Fine-Grained Document-Level Fact-Checking and Correction of LLM Output

Y Wang, RG Reddy, ZM Mujahid, A Arora… - arxiv preprint arxiv …, 2023 - arxiv.org
The increased use of large language models (LLMs) across a variety of real-world
applications calls for mechanisms to verify the factual accuracy of their outputs. In this work …