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Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination The Berkeley Artificial Intelligence Research Blog

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Sample language model responses to different varieties of English and native speaker reactions.

ChatGPT does amazingly well at communicating with people in English. But whose English?

Only 15% of ChatGPT users are from the US, where Standard American English is the default. But the model is also commonly used in countries and communities where people speak other varieties of English. Over 1 billion people around the world speak varieties such as Indian English, Nigerian English, Irish English, and African-American English.

Speakers of these non-“standard” varieties often face discrimination in the real world. They’ve been told that the way they speak is unprofessional or incorrect, discredited as witnesses, and denied housing–despite extensive research indicating that all language varieties are equally complex and legitimate. Discriminating against the way someone speaks is often a proxy for discriminating against their race, ethnicity, or nationality. What if ChatGPT exacerbates this discrimination?

To answer this question, our recent paper examines how ChatGPT’s behavior changes in response to text in different varieties of English. We found that ChatGPT responses exhibit consistent and pervasive biases against non-“standard” varieties, including increased stereotyping and demeaning content, poorer comprehension, and condescending responses.

Read More »Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination The Berkeley Artificial Intelligence Research Blog

Diagram of Thought (DoT): An AI Framework that Models Iterative Reasoning in Large Language Models (LLMs) as the Construction of a Directed Acyclic Graph (DAG) within a Single Model Shoaib Nazir Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Previous research on reasoning frameworks in large language models (LLMs) has explored various approaches to enhance problem-solving capabilities. Chain-of-Thought (CoT) introduced articulated reasoning processes, while Tree-of-Thought (ToT) and Graph-of-Thought (GoT) expanded on this concept by incorporating branching possibilities and complex relationships between reasoning steps.… Read More »Diagram of Thought (DoT): An AI Framework that Models Iterative Reasoning in Large Language Models (LLMs) as the Construction of a Directed Acyclic Graph (DAG) within a Single Model Shoaib Nazir Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Neural networks are widely adopted in various fields due to their ability to model complex patterns and relationships. However, they face a critical vulnerability to adversarial attacks – small, malicious input changes that cause unpredictable outputs. This issue poses significant challenges to the reliability… Read More »LoRID: A Breakthrough Low-Rank Iterative Diffusion Method for Adversarial Noise Removal Sajjad Ansari Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” In recent research, a state-of-the-art technique has been introduced for utilizing Large Language Models (LLMs) to verify RDF (Resource Description Framework) triples, emphasizing the significance of providing traceable and verifiable reasoning. The fundamental building blocks of knowledge graphs (KGs) are RDF triples, which are… Read More »Verifying RDF Triples Using LLMs with Traceable Arguments: A Method for Large-Scale Knowledge Graph Validation Tanya Malhotra Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Novak Zivanic has made a significant contribution to the field of Natural Language Processing with the release of Embedić, a suite of Serbian text embedding models. These models are specifically designed for Information Retrieval and Retrieval-Augmented Generation (RAG) tasks. Specifically, the smallest model in… Read More »Embedić Released: A Suite of Serbian Text Embedding Models Optimized for Information Retrieval and RAG Asif Razzaq Artificial Intelligence Category – MarkTechPost

Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker Kanwaljit Khurmi AWS Machine Learning Blog

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