Remove research gender-bias-llm
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Researchers at Stanford University Expose Systemic Biases in AI Language Models

Marktechpost

In a new AI research paper, a team of researchers from Stanford Law School has investigated biases present in state-of-the-art large language models (LLMs), including GPT-4, focusing particularly on disparities related to race and gender.

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This AI Research from Apple Investigates a Known Issue of LLMs’ Behavior with Respect to Gender Stereotypes

Marktechpost

Large language models (LLMs) have made tremendous strides in the last several months, crushing state-of-the-art benchmarks in many different areas. There has been a meteoric rise in people using and researching Large Language Models (LLMs), particularly in Natural Language Processing (NLP).

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How Human Bias Undermines AI-Enabled Solutions

Unite.AI

DeepMind, a Google-owned research lab that focuses on AI, recently published a study in which they proposed a three-tiered structure to evaluate the risks of AI, including both social and ethical risks. With over 100 million users , ChatGPT is one of the most successful LLMs, and it has often been accused of bias.

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Prompt Hacking and Misuse of LLMs

Unite.AI

GPT-4 is a type of LLM called an auto-regressive model which is based on the transformers model. How LLM generates output Once GPT-4 starts giving answers, it uses the words it has already created to make new ones. The Architecture: LLM and Its Vulnerabilities LLMs, especially those like GPT-4, are built on a Transformer architecture.

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Vivek Desai, Chief Technology Officer, North America at RLDatix – Interview Series

Unite.AI

Right now, we can easily train a LLM to read through text in an incident report. If a patient passes away, for example, the LLM can seamlessly pick out that information. If a patient passes away, for example, the LLM can seamlessly pick out that information. An important focus for us is mitigating bias and unfairness.

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CMU Researchers Introduce ReLM: An AI System For Validating And Querying LLMs Using Standard Regular Expressions

Marktechpost

There are rising worries about the potential negative impacts of large language models (LLMs), such as data memorization, bias, and unsuitable language, despite LLMs’ widespread praise for their capacity to generate natural-sounding text. Regular Expression engine for LMs, or ReLM for short.

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How RLHF Preference Model Tuning Works (And How Things May Go Wrong)

AssemblyAI

Much of current AI research aims to design LLMs that seek helpful, truthful, and harmless behavior. This piece should be helpful to anyone who wants a better understanding of LLMs and the challenges in making them safe and reliable. But how do we interpret the effect of RLHF fine-tuning over the original base LLM?

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