#evaluations + #llm

SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields conflicting, noisy, or unhelpful results. SealQA comes in three flavors: (1) Seal-0 (main) and (2) Seal-Hard, which assess factual accuracy and reasoning capabilities, with Seal-0 focusing on the most challenging questions where chat models (e.g., GPT-4.1) typically achieve near-zero accuracy; and (3) LongSeal, which extends SealQA to test long-context, multi-document reasoning in "needle-in-a-haystack" settings. Our evaluation reveals critical limitations in current models: Even frontier LLMs perform poorly across all SealQA flavors. On Seal-0, frontier agentic models equipped with tools like o3 and o4-mini achieve only 17.1% and 6.3% accuracy, respectively, at their best reasoning efforts. We find that advanced reasoning models such as DeepSeek-R1-671B and o3-mini are highly vulnerable to noisy search results. Notably, increasing test-time compute does not yield reliable gains across o3-mini, o4-mini, and o3, with performance often plateauing or even declining early.

benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam, a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. The dataset consists of 2,500 challenging questions across over a hundred subjects. We publicly release these questions, while maintaining a private test set of held out questions to assess model overfitting.

Accuracy. All frontier models achieve low accuracy on Humanity's Last Exam, highlighting significant room for improvement in narrowing the gap between current LLMs and expert-level academic capabilities on closed-ended questions.

Hahaha current state of the art is Gemini 3 Pro w/ 38.3:

Given the rapid pace of AI development, it is plausible that models could exceed 50% accuracy on HLE by the end of 2025.

Data and code for our paper FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation.

We believe that human evaluators possess the expertise and common sense required to detect issues like hallucinations, making them more reliable than automated evaluation metrics for assessing LLMs' factuality. However, researchers have the flexibility to adjust their evaluation methods if human evaluation proves challenging. An easily implemented alternative is to use standard metrics like F1/exact match or recall, which assess the overlap between the model response and the ground truth answer(s) (e.g., see You.com's recent blog where they report FreshQA recall). Researchers can also use LLM-based automatic evaluation metrics such as FactScore or our FreshEval metric below.

To facilitate future evaluations, we have developed FreshEval, a simple automatic metric that uses few-shot in-context learning to teach an LLM to judge model responses, which achieved high agreement with human raters (see Appendix B in our paper for details).

To use FreshEval under a specific evaluation mode (Relaxed or Strict), please follow the instructions below:

Make a copy of our latest data spreadsheet and store it in your Google Drive with a new filename (e.g., fresheval_relaxed or fresheval_strict).
Insert 3 new columns D, E, F in the new spreadsheet for model responses, evaluation rating, evaluation explanation, respectively and save your model's responses in column D (see our sample evaluation spreadsheet below).
Run the associated FreshEval notebook with the evaluation mode. Note that for demonstration purposes, we evaluated only the first 10 model responses. You can adjust the number as needed.

Note: Currently, we recommend gpt-4-1106-preview over gpt-4-0125-preview for FreshEval as it yielded slightly better agreement with human annotations in our small-scale evaluation.

we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked.

Through human evaluations involving more than 50K judgments, we shed light on limitations of these models and demonstrate significant room for improvement: for instance, all models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises.

we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA by incorporating relevant and up-to-date information retrieved from a search engine into the prompt. Our experiments show that FreshPrompt outperforms both competing search engine-augmented prompting methods such as Self-Ask (Press et al., 2022) as well as commercial systems such as this http URL. Further analysis of FreshPrompt reveals that both the number of retrieved evidences and their order play a key role in influencing the correctness of LLM-generated answers. Additionally, instructing the LLM to generate concise and direct answers helps reduce hallucination compared to encouraging more verbose answers.

FRAMES offers a unified framework for assessing LLM performance in end-to-end RAG scenarios. Our dataset comprises challenging multi-hop questions requiring integration of information from multiple sources. Baseline results show that even state-of-the-art LLMs struggle, achieving 0.408 accuracy without retrieval. However, our proposed multi-step retrieval pipeline significantly improves accuracy to 0.66 (>50% improvement).

Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.

If you are building with LLMs, creating high quality evals is one of the most impactful things you can do. Without evals, it can be very difficult and time intensive to understand how different model versions might affect your use case.

July 2025: simple-evals will no longer be updated for new models or benchmark results. The repo will continue to host reference implementations for HealthBench, BrowseComp, and SimpleQA.

Evals are sensitive to prompting, and there's significant variation in the formulations used in recent publications and libraries. Some use few-shot prompts or role playing prompts ("You are an expert software programmer..."). These approaches are carryovers from evaluating base models (rather than instruction/chat-tuned models) and from models that were worse at following instructions.

For this library, we are emphasizing the zero-shot, chain-of-thought setting, with simple instructions like "Solve the following multiple choice problem". We believe that this prompting technique is a better reflection of the models' performance in realistic usage.

We will not be actively maintaining this repository and monitoring PRs and Issues. In particular, we're not accepting new evals. Here are the changes we might accept.

Bug fixes (hopefully not needed!)
Adding adapters for new models
Adding new rows to the table below with eval results, given new models and new system prompts.

This repository is NOT intended as a replacement for https://github.com/openai/evals, which is designed to be a comprehensive collection of a large number of evals.

Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides:

Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.
Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.
Experiments - Track and evaluate changes to prompts, LLMs, and retrieval.
Playground- Optimize prompts, compare models, adjust parameters, and replay traced LLM calls.
Prompt Management- Manage and test prompt changes systematically using version control, tagging, and experimentation.