Title: On the Worst Prompt Performance of Large Language Models
Benchmark: RobustAlpacaEval
Summary: This paper introduces RobustAlpacaEval, a benchmark to evaluate the worst-case prompt performance of LLMs across semantically equivalent real-world queries. It shows that ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families are highly sensitive to prompt phrasing, that characterizing the worst prompt is difficult, and that common techniques for improving prompt robustness offer limited success.
Phenomenon type: General Capability (A broadly useful ability, which could be relevant to multiple applications)
Target phenomenon: Prompt robustness
Phenomenon defined by authors: Yes
Phenomenon definition: "Resilience of LLMs to prompt variations." In particular, "model performance across semantically equivalent and syntactically fluent prompts."
Definition scope: Subset
Definition integrity: Single cohesive phenomenon
Definition integrity detail: Not applicable
Phenomenon contested: Widely-agreed
Task definition: Follow the instructions presented in 10 semantically-equivalent prompts.
Task item definition: The task dataset has 2 columns: "instruction" contains the original instruction, and "paraphrases" contains 10 semantically-equivalent paraphrases of the original instruction.
Task source: Modified from another benchmark (e.g. translation into another language), LLM-generated task examples (e.g. Filtered from responses to a prompt)
Task source detail: The original instruction is taken from an existing benchmark and the paraphrases are synthetically generated with GPT4 and then manually reviewed.
Task ecology detail: People would not ask a model 10 times the same but they expect the same answer no matter the wording.
Dataset size: 100
Dataset metadata available: No
Sampling method: Convenience sample (creators found a set of tasks that was readily accessible)
Split reviewed: Test
Response format: Free response (e.g. summary paragraph, executable code)
Metric definition: LLM-as-a-Judge (text quality, preferences, NOT extracting answers for other metrics)
Metric aggregation: Simple Mean
Metric statistics: Mean, worst and best out of 11
Metric subscores: No
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Mix (multiple authors from industry and academia)
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: Yes
Results comparison explanation: Yes
Results realism: No
Human baseline: No
Authors discuss validity: No
Taxonomy: Language Modelling / Robustness
New taxonomy: General Capability / Reliability