Innodata’s Comprehensive Benchmarking of Llama2, Mistral, Gemma, and GPT for Factuality, Toxicity, Bias, and Hallucination Propensity

Innodata’s Comprehensive Benchmarking of Llama2, Mistral, Gemma, and GPT for Factuality, Toxicity, Bias, and Hallucination Propensity

Practical Solutions and Value of AI Benchmarking Study

Practical Solutions

The study evaluated large language models (LLMs) such as Llama2, Mistral, Gemma, and GPT across key safety metrics: factuality, toxicity, bias, and propensity for hallucinations.

Value

The research introduced novel datasets and benchmarking tools to evaluate the safety and reliability of LLMs for diverse applications in enterprise and consumer environments.

Key Findings from the Study

Llama2

Performed well in factuality and handling toxic content, making it suitable for applications requiring reliable and safe responses. However, it needs improvement in avoiding hallucinations and safety in multi-turn interactions.

Mistral

Avoided hallucinations and excelled in multi-turn conversations but struggled with toxicity detection, limiting its application in contexts requiring safety from offensive content.

Gemma

Displayed balanced performance but lagged behind in overall effectiveness, with a tendency to refuse biased prompts, limiting its usability in certain contexts.

OpenAI GPT

Outperformed smaller open-source models across safety vectors, especially in reducing “laziness” and maintaining high safety standards, highlighting the advanced engineering and larger parameter sizes of OpenAI models.

Importance of Comprehensive Safety Evaluations for LLMs

Emphasized the need for ongoing and future research to improve the safety and reliability of LLMs in diverse applications, especially in enterprise environments.

Conclusion

While Llama2, Mistral, and Gemma show promise, there is room for improvement. OpenAI’s GPT models set a high benchmark for safety and performance, demonstrating the potential benefits of advancements and refinements in LLM technology.

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