How to Rigorously Test Your Own Trained LLM and Choose the Right Benchmarks

Evaluation Goals

Three evaluation objectives are distinguished: Internal iteration testing to detect improvements or regressions for the development team; External release testing to provide reproducible evidence for researchers, developers, and users; and Production deployment testing to verify that the model meets SLA requirements on real‑task data, with public benchmarks serving only as references.

Core Capability Benchmarks

MMLU‑Pro – an expanded version of MMLU with ten answer choices per question, covering 14 disciplines and still discriminative in 2026.

GPQA Diamond – graduate‑level, Google‑proof Q&A designed by domain experts; questions cannot be answered via search engines, reducing data‑pollution risk.

MATH‑500 / AIME – competition‑level mathematics requiring multi‑step symbolic reasoning; AIME 2025/2026 real‑exam questions are recommended for low‑pollution risk. Example scores: Qwen3.5‑plus 91.3% on AIME 2026, GPT‑5.3 Codex 94% on AIME 2025.

IFEval – tests precise instruction following (e.g., "output must be JSON", "no more than 200 words"); reports both Prompt‑level and Instruction‑level accuracy.

Code and Engineering Benchmarks

SWE‑Bench Verified – extracts real GitHub Python issues; models must locate bugs, modify code, and pass tests within full repository context. The Verified subset contains 500 manually verified items.

HumanEval+ / MBPP+ – expanded from the original HumanEval and MBPP with broader edge cases, eliminating the "incorrect code passes" problem; suitable for function‑level code generation baselines.

LiveCodeBench – continuously crawls new problems from LeetCode, Codeforces, AtCoder, ensuring test data post‑date the model’s training cutoff.

Agent Capability Benchmarks

BFCL – Berkeley Function Calling Leaderboard; measures tool‑calling precision across simple, composite, multi‑step, and cross‑language calls, with negative tests for erroneous tool activation.

GAIA – General AI Assistant benchmark with 466 handcrafted assistant tasks at three difficulty levels; Level 1 and Level 2 are recommended core reports.

AgentBench – Tsinghua University suite covering eight independent environments (OS tasks, database queries, KG reasoning, web shopping, browsing, etc.) to assess cross‑scenario generalisation.

τ²‑Bench – vertical‑business benchmark simulating telephone‑customer‑service constraints, testing rule‑bound request handling and intent uncertainty.

Safety and Reliability Benchmarks

TruthfulQA – 817 questions designed to trap models that give plausible but false answers; rewarding "I don’t know" responses.

MT‑Bench / Arena‑Hard – 80‑question (MT‑Bench) and 500‑question (Arena‑Hard) multi‑turn dialogues scored by GPT‑4; provides realistic user‑experience metrics but inherits GPT‑4 bias.

SimpleQA / AA‑Omniscience – measures factual accuracy and refusal rate; AA‑Omniscience adds detailed hallucination scoring (correct + penalty, wrong – penalty, refusal = 0).

Data Contamination Mitigation

Four strategies are recommended:

Prefer benchmarks with recent timestamps (e.g., LiveCodeBench, AIME 2025/2026) that post‑date the training data.

Publish n‑gram overlap rates (typically 13‑gram) between training corpora and test sets.

Run random‑control tests using semantically equivalent rewrites; a gap >5 % warrants investigation.

Choose datasets designed to avoid searchable answers (e.g., GPQA Diamond).

Toolchain and Runtime Frameworks

lm‑evaluation‑harness – community standard used by HuggingFace Open LLM Leaderboard; includes hundreds of benchmarks. For instruction‑tuned models, add --apply_chat_template and --fewshot_as_multiturn. Precision settings (bfloat16/float16) must be declared; scores across versions are not directly comparable.

EvalPlus – dedicated framework for HumanEval+ and MBPP+, providing sandboxed code execution for higher reliability.

SWE‑bench official framework – required to obtain comparable results; runs each task in an isolated Docker container.

FastChat – official runner for MT‑Bench, embedding GPT‑4 evaluation logic and supporting batch evaluation.

Publishing Evaluation Results

When releasing a model, disclose at minimum:

Model weights and API access details.

Framework version (e.g., lm‑eval version) – scores are not comparable across versions.

Precision setting (bf16, fp16, int8) – directly impacts scores.

Few‑shot configuration (0‑shot vs 5‑shot can cause large differences).

Whether a chat template was used for instruction‑tuned models.

Data‑contamination check results (n‑gram overlap or methodology).

Training data cutoff date – helps assess contamination risk.

Structural Limitations of Evaluation

Static benchmarks cannot measure real‑world usefulness; dynamic user‑preference tests like Chatbot Arena capture practical value.

Benchmarks saturate roughly every 12–18 months; once top models exceed ~85 % accuracy, the test loses discriminative power.

Goodhart’s Law: when a benchmark becomes a target (e.g., SWE‑Bench), models may over‑fit to its format, decoupling scores from true generalisation.

Minimal Evaluation Checklist for Resource‑Limited Teams

General LLM – mandatory: MMLU‑Pro, GPQA Diamond, MATH‑500, IFEval; recommended: TruthfulQA, MT‑Bench.

Code/Engineering Model – add: SWE‑Bench Verified, HumanEval+, LiveCodeBench.

Agent Model – add: BFCL, GAIA Level 1 & 2; recommended: SWE‑Bench Verified, AgentBench.

References

lm‑evaluation‑harness – github.com/EleutherAI/lm-evaluation-harness

Open LLM Leaderboard – huggingface.co/open-llm-leaderboard

SWE‑Bench – swebench.com

BFCL – gorilla.cs.berkeley.edu/leaderboard.html

GAIA – huggingface.co/gaia-benchmark

EvalPlus – evalplus.github.io

LiveCodeBench – livecodebench.github.io

AgentBench – github.com/THUDM/AgentBench

FastChat – github.com/lm-sys/FastChat