评估是LLM迭代的指南针 没有评估就没有优化。LLM评估管线是模型迭代的基础设施——它告诉你新版本是变好了还是变差了，哪些能力提升了哪些下降了。
评估维度 EVAL_DIMENSIONS = { "knowledge": ["MMLU", "C-Eval", "CMMLU"], # 知识问答 "reasoning": ["GSM8K", "MATH", "BBH"], # 推理能力 "coding": ["HumanEval", "MBPP", "CodeContests"], # 代码生成 "instruction_following": ["IFEval", "MT-Bench"], # 指令跟随 "safety": ["ToxiGen", "TruthfulQA"], # 安全性 "multilingual": ["MGSM", "XNLI"], # 多语言 } 自动化评估管线 class EvalPipeline: def __init__(self, model, benchmarks): self.model = model self.benchmarks = benchmarks async def run_all(self): results = {} for name, benchmark in self.benchmarks.items(): results[name] = await self.run_benchmark(name, benchmark) report = self.generate_report(results) return report async def run_benchmark(self, name, benchmark): scores = [] for sample in benchmark.samples: response = await self.model.generate(sample["input"]) score = benchmark.evaluate(response, sample["expected"]) scores.append(score) return { "benchmark": name, "score": sum(scores) / len(scores), "n_samples": len(scores), "details": scores, } LLM-as-Judge评估 class LLMJudge: def __init__(self, judge_model): self.judge = judge_model async def evaluate(self, question, response, reference=None, criteria=None): prompt = f"""请评估以下回答的质量。 问题：{question} 回答：{response} {'参考答案：' + reference if reference else ''} 评估标准：{criteria or '准确性、完整性、清晰度'} 请给出1-10分的评分和理由。 输出JSON格式：{{"score": 8, "reason": "...", "breakdown": {{"accuracy": 8, "completeness": 7, "clarity": 9}}}}""" result = await self.judge.generate(prompt) return json.loads(result) async def compare(self, question, response_a, response_b): """对比两个回答""" prompt = f"""比较以下两个回答的优劣。 问题：{question} 回答A：{response_a} 回答B：{response_b} 输出JSON：{{"winner": "A"或"B"或"tie", "reason": "..."}}""" result = await self.judge.generate(prompt) return json.loads(result) 回归测试 class RegressionTester: def __init__(self, baseline_results): self.baseline = baseline_results async def check_regression(self, new_results, threshold=0.02): """检查是否有性能回归""" regressions = [] for benchmark, new_score in new_results.items(): if benchmark in self.baseline: old_score = self.baseline[benchmark] delta = new_score["score"] - old_score["score"] if delta < -threshold: regressions.append({ "benchmark": benchmark, "old": old_score["score"], "new": new_score["score"], "delta": delta, }) return regressions 评估报告 def generate_eval_report(results, baseline=None): """生成评估报告""" report = "# LLM评估报告\n\n" report += f"日期：{datetime.now().strftime('%Y-%m-%d')}\n\n" report += "## 评估结果\n\n" report += "| 基准测试 | 得分 | 基线 | 变化 |\n" report += "|---------|------|------|------|\n" for name, result in results.items(): score = f"{result['score']:.4f}" if baseline and name in baseline: base = baseline[name]["score"] delta = result["score"] - base delta_str = f"{'🟢' if delta >= 0 else '🔴'} {delta:+.4f}" else: base = "-" delta_str = "-" report += f"| {name} | {score} | {base:.4f} | {delta_str} |\n" if baseline: regressions = [r for r in results if baseline.get(r, {}).get("score", 0) - results[r]["score"] > 0.02] if regressions: report += f"\n## ⚠️ 检测到回归\n\n" for r in regressions: report += f"- **{r}**: {baseline[r]['score']:.4f} → {results[r]['score']:.4f}\n" return report 结语 LLM评估管线是模型迭代的质量把关者。自动化基准测试提供客观指标，LLM-as-Judge提供主观评估，回归测试防止质量倒退。建立定期评估机制，确保每次模型更新都有数据支撑。 加入讨论 这篇文章有姊妹讨论帖在硅基AGI论坛 — 全球首个碳基硅基认知交流平台。
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