Rongchai Wang
Jul 29, 2025 01:59
Together AI unveils Together Evaluations, a framework for benchmarking large language models using open-source models as judges, offering customizable insights into model performance.
Together AI has announced the launch of Together Evaluations, a new framework designed to benchmark the performance of large language models (LLMs) using open-source models as judges. This innovative approach aims to provide fast and customizable insights into model quality, eliminating the need for manual labeling and rigid metrics, according to together.ai.
Revolutionizing Model Evaluation
The introduction of Together Evaluations addresses the challenges faced by developers in keeping up with the rapid evolution of LLMs. By utilizing task-specific benchmarks and strong AI models as judges, developers can quickly compare model responses and assess performance without the overhead of traditional methods.
This framework allows users to define benchmarks tailored to their specific needs, offering flexibility and control over the evaluation process. The use of LLMs as judges accelerates the evaluation process and provides a more adaptable metric system compared to traditional approaches.
Evaluation Modes and Use Cases
Together Evaluations offers three distinct modes: Classify, Score, and Compare. Each mode is powered by LLMs that users can fully control through prompt templates:
- Classify: Assigns samples to chosen labels, aiding in tasks like identifying policy violations.
- Score: Generates numeric ratings, useful for gauging relevance or quality on a defined scale.
- Compare: Allows users to judge between two model responses, facilitating the selection of more concise or relevant outputs.
These evaluation modes provide aggregate metrics such as accuracy and mean scores, alongside detailed feedback from the judge, enabling developers to fine-tune their models effectively.
Practical Implementation
Together AI provides comprehensive support for integrating Together Evaluations into existing workflows. Developers can upload data in JSONL or CSV formats and choose the appropriate evaluation type. The framework supports a wide range of models, allowing for extensive testing and validation of LLM outputs.
For those interested in exploring the capabilities of Together Evaluations, the platform offers practical demonstrations and Jupyter notebooks showcasing real-world applications of LLM-as-a-judge workflows. These resources are designed to help developers understand and implement the framework effectively.
Conclusion
As the field of LLM-driven applications continues to mature, Together AI’s introduction of Together Evaluations represents a significant step forward in enabling developers to efficiently benchmark and refine their models. This framework not only simplifies the evaluation process but also enhances the ability to choose and optimize models based on specific task requirements.
Developers and AI enthusiasts are invited to participate in a practical walkthrough on July 31st, where Together AI will demonstrate how to leverage Together Evaluations for various use cases, further solidifying its commitment to supporting the AI community.
Image source: Shutterstock
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