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LLM Evaluation Framework

LLM Evaluation Framework

7
posts
2025–2026

Together Evaluations now supports OpenAI, Anthropic, and Google models for comprehensive benchmarking. This enables users to compare any models side-by-side—open-source, fine-tuned, or proprietary—to make data-driven decisions on quality, cost, and performance. The framework now supports evaluating Together fine-tuned models and includes new recipes for optimizing and evaluating open-source models, demonstrating how fine-tuned open models can outperform proprietary judges at significantly lower cost and higher speed, and how automated prompt optimization can deliver substantial quality gains.

2026

What do LLMs think when you don't tell them what to think about?

2/6/2026

This post introduces the concept of studying LLM behavior under near-unconstrained generation (minimal, topic-neutral prompts) to reveal inherent model priors. It details findings on distinct topical preferences across model families (GPT-OSS favoring programming/math, Llama leaning literary, DeepSeek religious, Qwen multiple-choice), depth differences in technical content generation, and model-specific degenerate output patterns (e.g., URLs, formatting artifacts). This expands the LLM Evaluation Framework by adding a new dimension of analysis beyond task-specific prompting.

Together Evaluations now supports comparing top commercial APIs vs. open source models

2/2/2026

This post extends the LLM Evaluation Framework by adding support for proprietary model providers (OpenAI, Anthropic, Google) as both judge and target models. It also enables evaluation of Together fine-tuned models (LoRA serverless Inference, Dedicated Endpoints) and provides new recipes for optimizing and evaluating open-source models, including fine-tuning open models to outperform proprietary judges and using GEPA for automated prompt optimization.

How to choose the right open model for production

1/8/2026

This post provides guidance on selecting open-source LLMs for production workloads. It outlines the benefits of open models (transparency, adaptability, control), legal considerations (licensing, origin), and offers a framework for comparing models based on parameter size and capability tiers (low, medium, high). It details trade-offs between cost, speed, and quality, providing example configurations. The post emphasizes the importance of rigorous evaluation using metrics, a golden dataset, and techniques like LLM-as-a-judge, with a step-by-step process for manual review and prompt modification. It also highlights the role of fine-tuning (LoRA SFT, DPO) for task-specific adaptation.

2025

How to evaluate and benchmark Large Language Models (LLMs)

11/4/2025

This post elaborates on the principles of effective LLM benchmarking, detailing five key characteristics: difficulty, diversity, usefulness, reproducibility, and data contamination. It provides concrete examples and visual aids to illustrate these concepts, such as the benchmark saturation phenomenon, the importance of multi-domain testing (MixEval), the connection between benchmarks like GSM8K and HumanEval to real-world use cases, the challenges of reproducibility with MMLU, and the problem of data contamination using GSM8K vs. GSM1K and USAMO 2025 examples. It also outlines different LLM evaluation methods, including multiple-choice, classification, and generation-based benchmarks, and emphasizes the need for practical code notebooks for real-world LLM performance testing.

Dynamic AI agent testing for the real world with Collinear Simulations and Together Evals

10/28/2025

Introduces the integration of Collinear's TraitMix simulation product with Together Evals. TraitMix generates dynamic, persona-driven AI agent interactions by mixing user traits (e.g., impatience, confusion, sarcasm) to create realistic, multi-turn conversational data. This data is then automatically judged using Together Evals' LLM-as-a-judge framework, enabling reproducible and scalable testing of AI agents under human variability. The post highlights the ability to close the loop between interaction, evaluation, and improvement within a single ecosystem.

Large Reasoning Models Fail to Follow Instructions During Reasoning: A Benchmark Study

10/22/2025

This post introduces the ReasonIF benchmark, a systematic evaluation for assessing instruction-following abilities within the reasoning traces of Large Reasoning Models (LRMs). It highlights that frontier LRMs fail to follow reasoning instructions more than 75% of the time, with performance degrading further as task difficulty increases. The benchmark consists of 300 math and science problems paired with six types of verifiable user-oriented directives (multilinguality, word limit, disclaimer, JSON formatting, uppercase only, remove commas) that models must obey throughout their step-by-step solutions. The post presents findings that demonstrate a significant drop in instruction-following scores within reasoning traces compared to main responses, even for top-performing models.

Together Evaluations: Benchmark Models for Your Tasks

7/28/2025

Introduces Together Evaluations, a new framework for benchmarking LLM response quality. This framework enables users to define custom benchmarks and use LLMs as judges to evaluate model performance across 'Classify', 'Score', and 'Compare' modes. It details data upload formats (JSONL, CSV), system template configuration, and model selection for evaluation. The post also highlights the integration with existing serverless inference APIs and provides links to documentation, UI, and tutorial notebooks.