Benchmark reference¶
This reference records the original research benchmark. Counts and metrics below describe that benchmark, not a live inventory of every downloaded file. Use python -m trustllm tasks to inspect the current task registry and record dataset hashes for your experiment. See the paper for methodology and the scoring guide for the maintained workflow.
Dataset overview¶
✓ the dataset is from prior work, and ✗ means the dataset is first proposed in our benchmark.
| Dataset | Description | Num. | Exist? | Section |
|---|---|---|---|---|
| SQuAD2.0 | It combines questions in SQuAD1.1 with over 50,000 unanswerable questions. | 100 | ✓ | Misinformation |
| CODAH | It contains 28,000 commonsense questions. | 100 | ✓ | Misinformation |
| HotpotQA | It contains 113k Wikipedia-based question-answer pairs for complex multi-hop reasoning. | 100 | ✓ | Misinformation |
| AdversarialQA | It contains 30,000 adversarial reading comprehension question-answer pairs. | 100 | ✓ | Misinformation |
| Climate-FEVER | It contains 7,675 climate change-related claims manually curated by human fact-checkers. | 100 | ✓ | Misinformation |
| SciFact | It contains 1,400 expert-written scientific claims pairs with evidence abstracts. | 100 | ✓ | Misinformation |
| COVID-Fact | It contains 4,086 real-world COVID claims. | 100 | ✓ | Misinformation |
| HealthVer | It contains 14,330 health-related claims against scientific articles. | 100 | ✓ | Misinformation |
| TruthfulQA | The multiple-choice questions to evaluate whether a language model is truthful in generating answers to questions. | 352 | ✓ | Hallucination |
| HaluEval | It contains 35,000 generated and human-annotated hallucinated samples. | 300 | ✓ | Hallucination |
| LM-exp-sycophancy | A dataset consists of human questions with one sycophancy response example and one non-sycophancy response example. | 179 | ✓ | Sycophancy |
| Opinion pairs | It contains 120 pairs of opposite opinions. | 240, 120 | ✗ | Sycophancy, Preference |
| WinoBias | It contains 3,160 sentences, split for development and testing, created by researchers familiar with the project. | 734 | ✓ | Stereotype |
| StereoSet | It contains the sentences that measure model preferences across gender, race, religion, and profession. | 734 | ✓ | Stereotype |
| Adult | The dataset, containing attributes like sex, race, age, education, work hours, and work type, is utilized to predict salary levels for individuals. | 810 | ✓ | Disparagement |
| Jailbreak Trigger | The dataset contains the prompts based on 13 jailbreak attacks. | 1300 | ✗ | Jailbreak, Toxicity |
| Misuse (additional) | This dataset contains prompts crafted to assess how LLMs react when confronted by attackers or malicious users seeking to exploit the model for harmful purposes. | 261 | ✗ | Misuse |
| Do-Not-Answer | It is curated and filtered to consist only of prompts to which responsible LLMs do not answer. | 344 + 95 | ✓ | Misuse, Stereotype |
| AdvGLUE | A multi-task dataset with different adversarial attacks. | 912 | ✓ | Natural Noise |
| AdvInstruction | 600 instructions generated by 11 perturbation methods. | 600 | ✗ | Natural Noise |
| ToolE | A dataset with the users' queries which may trigger LLMs to use external tools. | 241 | ✓ | Out of Domain (OOD) |
| Flipkart | A product review dataset, collected starting from December 2022. | 400 | ✓ | Out of Domain (OOD) |
| DDXPlus | A 2022 medical diagnosis dataset comprising synthetic data representing about 1.3 million patient cases. | 100 | ✓ | Out of Domain (OOD) |
| ETHICS | It contains numerous morally relevant scenarios descriptions and their moral correctness. | 500 | ✓ | Implicit Ethics |
| Social Chemistry 101 | It contains various social norms, each consisting of an action and its label. | 500 | ✓ | Implicit Ethics |
| MoralChoice | It consists of different contexts with morally correct and wrong actions. | 668 | ✓ | Explicit Ethics |
| ConfAIde | It contains the description of how information is used. | 196 | ✓ | Privacy Awareness |
| Privacy Awareness | It includes different privacy information queries about various scenarios. | 280 | ✗ | Privacy Awareness |
| Enron Email | It contains approximately 500,000 emails generated by employees of the Enron Corporation. | 400 | ✓ | Privacy Leakage |
| Xstest | It's a test suite for identifying exaggerated safety behaviors in LLMs. | 200 | ✓ | Exaggerated Safety |
Task overview¶
○ means evaluation through the automatic scripts (e.g., keywords matching), ● means the automatic evaluation by ChatGPT, GPT-4 or longformer, and ◐ means the mixture evaluation.
More trustworthy LLMs are expected to have a higher value of the metrics with ↑ and a lower value with ↓.
| Task Name | Metrics | Type | Eval | Section |
|---|---|---|---|---|
| Closed-book QA | Accuracy (↑) | Generation | ○ | Misinformation(Internal) |
| Fact-Checking | Macro F-1 (↑) | Classification | ● | Misinformation(External) |
| Multiple Choice QA | Accuracy (↑) | Classification | ● | Hallucination |
| Hallucination Classification | Accuracy (↑) | Classification | ● | Hallucination |
| Persona Sycophancy | Embedding similarity (↑) | Generation | ◐ | Sycophancy |
| Opinion Sycophancy | Percentage change (↓) | Generation | ○ | Sycophancy |
| Factuality Correction | Percentage change (↑) | Generation | ○ | Adversarial Factuality |
| Jailbreak Attack Evaluation | RtA (↑) | Generation | ○ | Jailbreak |
| Toxicity Measurement | Toxicity Value (↓) | Generation | ● | Toxicity |
| Misuse Evaluation | RtA (↑) | Generation | ○ | Misuse |
| Exaggerated Safety Evaluation | RtA (↓) | Generation | ○ | Exaggerated Safety |
| Agreement on Stereotypes | Accuracy (↑) | Generation | ◐ | Stereotype |
| Recognition of Stereotypes | Agreement Percentage (↓) | Classification | ◐ | Stereotype |
| Stereotype Query Test | RtA (↑) | Generation | ○ | Stereotype |
| Preference Selection | RtA (↑) | Generation | ○ | Preference |
| Salary Prediction | p-value (↑) | Generation | ● | Disparagement |
| Adversarial Perturbation in Downstream Tasks | ASR (↓), RS (↑) | Generation | ◐ | Natural Noise |
| Adversarial Perturbation in Open-Ended Tasks | Embedding similarity (↑) | Generation | ◐ | Natural Noise |
| OOD Detection | RtA (↑) | Generation | ○ | Out of Domain (OOD) |
| OOD Generalization | Micro F1 (↑) | Classification | ○ | Out of Domain (OOD) |
| Agreement on Privacy Information | Pearson's correlation (↑) | Classification | ● | Privacy Awareness |
| Privacy Scenario Test | RtA (↑) | Generation | ○ | Privacy Awareness |
| Probing Privacy Information Usage | RtA (↑), Accuracy (↓) | Generation | ◐ | Privacy Leakage |
| Moral Action Judgement | Accuracy (↑) | Classification | ◐ | Implicit Ethics |
| Moral Reaction Selection (Low-Ambiguity) | Accuracy (↑) | Classification | ◐ | Explicit Ethics |
| Moral Reaction Selection (High-Ambiguity) | RtA (↑) | Generation | ○ | Explicit Ethics |
| Emotion Classification | Accuracy (↑) | Classification | ● | Emotional Awareness |