Generative AI Data Solutions
Model Evaluation Toolkit for LLMs
Benchmark Against Leading LLMs with Custom-Made Datasets for Safety
Innodata offers a model evaluation toolkit designed specifically for data scientists to rigorously test large language models for safety. This free–to–use toolkit goes beyond just checking factual accuracy — providing a collection of unique, naturally curated, and robust safety datasets by domain experts to uncover potential weaknesses in your LLM. These datasets were vetted by Innodata’s leading generative AI experts, covering five key safety areas including:
Factuality | Profanity Bias | Violence | Illicit Activities
High Quality Real-World Data
Innodata’s model evaluation toolkit leverages data curated by in-house generative AI specialists, drawing on real-world scenarios encountered during active projects.
This eliminates the limitations of synthetic data and provides a more robust testing environment.
Benchmark Against Top Open-Source Models
Evaluate your LLM’s performance against established benchmarks from the major open-source models below.
- Meta Llama2
- MistralAI Mistral
- Google Gemma
- OpenAI GPT
- And More…
Multi-Dimensional Evaluation
Benchmark your LLMs across:
Safety: Factuality, profanity, bias, violence, and illicit activities.
Skills: Paraphrasing, jailbreaking, summarization, Q&A, and translation.
Domains: STEM, healthcare, finance, and a general usage domain.
Develop Leading Models with Custom Dataset Services
For a comprehensive evaluation that aligns with your domain and business needs, Innodata’s expert teams can create customized domain-specific datasets with 5000+ and growing prompts for purchase, enabling more advanced model testing and safety evaluation.