Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Accelerating Unbiased LLM Evaluation via Synthetic Feedback
Authors: Zhaoyi Zhou, Yuda Song, Andrea Zanette
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate a reduction in human annotations by up to 12.2% with an off-the-shelf synthetic evaluator and up to 24.8% with a finetuned variant. Our experiments demonstrate a reduction in human annotations by up to 12.2% with an off-the-shelf synthetic evaluator and up to 24.8% with a finetuned variant. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University. Correspondence to: Zhaoyi Zhou <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Control Variates Evaluation |
| Open Source Code | Yes | Our code is available at https://github.com/Zanette-Labs/control_variates_evaluation. |
| Open Datasets | Yes | Chat Bot Arena (Zheng et al., 2023) contains 33k human-annotated preferences. MT Bench (Zheng et al., 2023) contains about 3.36k human-annotated preferences. We utilize the validation split of the Help Steer2 dataset as our benchmark. |
| Dataset Splits | Yes | The testing of Control Variates with finetuning (Line 3 of Algorithm 1) is done in a crossvalidation manner. Suppose there are K LLMs generating responses in the evaluation dataset. Our finetuning procedure trains K reward models, each by leaving out the data for a specific LLM. We utilize the validation split of the Help Steer2 dataset as our benchmark. |
| Hardware Specification | Yes | The experiments are run on H100 GPUs. Finetuning Skywork-8B requires 4 GPUs. |
| Software Dependencies | No | The paper mentions models like GRM-Gemma-2Bsftreg, Armo RM-Llama38B, Skywork-Reward Llama-3.1-8B-v0.2, and GPT-4, and discusses learning rates and batch sizes, but does not provide specific version numbers for software libraries or frameworks used for implementation (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | When finetuning Skywork-8B and GRM-2B on Chatbot Arena and MT Bench, we use global batch size 32 and train for 1 epoch. The finetuning of GRM-2B on Chatbot Arena uses learning rate 1e-6, others all use learning rate 3e-6. We tested learning rates in {1 10 7, 3 10 7, 1 10 6, 3 10 6, 1 10 5, 3 10 5} and batch sizes in {32, 64, 128}. |