What do you know about LLM | What is Large Language model | What is LLM AI

AI Governance Expert

The challenges posed by biases in LLMs are significant, as they can perpetuate harmful stereotypes present in training data. Recent studies indicate that as biases become more pronounced, governance frameworks must evolve to ensure ethical compliance. Regulatory bodies are increasingly focusing on establishing standards for AI content generation to safeguard against unethical practices. For example, organizations like the Partnership on AI are leading initiatives to promote responsible AI development.

AI Data Scientist Expert

The complexity of evaluating LLMs using metrics like perplexity and BLEU score reflects the evolving landscape of machine learning. As generative models advance, it's vital to adopt comprehensive evaluation strategies that encompass not just linguistic accuracy but also the semantic depth of generated text. According to recent trends, integrating human feedback into the evaluation process may enhance model training iterations, aligning outputs more closely with user expectations in real-world applications.

Large Language Models

They are highlighted for their ability to process language contextually better than previous AI systems.

Perplexity

It's mentioned as a critical metric for determining the effectiveness of LLMs.

BLEU score

It is referenced as part of the assessment criteria for comparing LLM output against human-generated content.