Innodata’s Thorough Assessment of Language Models: Llama2, Mistral, Gemma, and GPT
Innodata, Inc., a leading provider of data collection solutions, conducted a comprehensive evaluation of four prominent generative AI models—Llama2, Mistral, Gemma, and GPT—to measure their performance in four key areas:
* Factuality: Accuracy of information generated
* Toxicity: Presence of harmful language
* Bias: Potential for biased or discriminatory output
* Hallucination Susceptibility: Tendency to generate factually incorrect content
Methodology
Innodata’s evaluation utilized a set of diverse prompts and scenarios designed to test the models’ capabilities and weaknesses. Human raters assessed the generated responses for accuracy, toxicity, bias, and hallucination susceptibility.
Results
Factuality
GPT demonstrated the highest level of factuality, followed by Gemma and Mistral. Llama2 exhibited the lowest factuality score, suggesting a higher risk of generating incorrect information.
Toxicity
Mistral and Gemma performed well in minimizing toxicity, with low occurrence of harmful language. Llama2 and GPT showed higher levels of toxicity, requiring more stringent filtering mechanisms.
Bias
All four models exhibited minimal bias across different demographics and perspectives. However, GPT demonstrated slightly higher susceptibility to bias in certain scenarios.
Hallucination Susceptibility
Llama2 exhibited the highest hallucination susceptibility, often generating factually incorrect or implausible content. GPT and Gemma performed better, with lower rates of hallucination. Mistral showed the lowest susceptibility to hallucination, providing the most reliable responses.
Implications
The findings of Innodata’s evaluation have significant implications for the deployment and use of generative AI models:
* Factuality: Models with lower factuality scores should be used cautiously, particularly in scenarios where accuracy is crucial.
* Toxicity: Models with higher toxicity levels require careful monitoring and filtering to prevent the spread of harmful content.
* Bias: Organizations should consider the potential for bias when using generative AI models to ensure fair and unbiased outcomes.
* Hallucination Susceptibility: Models prone to hallucinations may not be suitable for applications where factual accuracy is essential.
Conclusion
Innodata’s thorough assessment provides valuable insights into the strengths and weaknesses of leading generative AI models. By understanding the potential limitations and risks associated with these models, organizations can make informed decisions about their deployment and use.
Kind regards J.O. Schneppat
Artificial Intelligence News & Trends
