Title: The Greatest Good Benchmark: Measuring LLMs√¢‚Ç¨‚Ñ¢ Alignment with Utilitarian Moral Dilemmas
Benchmark: The Greatest Good Benchmark
Summary: The paper introduces the Greatest Good Benchmark (GGB) to evaluate LLMs moral judgments using utilitarian dilemmas. It analyzes a diverse set of models uncovering consistent moral preferences that diverge from established moral theories and lay population moral standards.
Phenomenon type: General Capability (A broadly useful ability, which could be relevant to multiple applications)
Target phenomenon: moral judgments
Phenomenon defined by authors: No
Definition scope: Subset
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Not defined
Task definition: examine utilitarian dilemmas presented as various statements and provide agreement/disagreement response
Task item definition: statement, subscale
Task source: Human exam questions (e.g. GRE questions), LLM-generated task examples (e.g. Filtered from responses to a prompt)
Task source detail: By human exam questions, I mean adapted from a scale used on humans
Task ecology: Representative task (e.g. answering medical licensing exam questions)
Dataset size: 540
Dataset metadata available: Yes
Dataset metadata detail: subscale, instruction variation
Sampling method: Convenience sample (creators found a set of tasks that was readily accessible)
Split reviewed: Test
Response format: Multiple choice
Metric definition: Human ratings (text quality, preference, NOT manual scoring of other metrics)
Metric aggregation: Simple Mean
Metric statistics: simple mean + standard errors, standard deviations
Metric subscores: Yes
Metric subscores detail: subscale
Metric access: Model access required (e.g. logits)
Few-shot evaluation: No
Authorship: Mix (multiple authors from industry and academia)
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: No
Results interpreted by authors: Yes
Results compared: No
Results comparison explanation: No comparisons made
Results realism: No
Human baseline: Yes
Authors discuss validity: Yes
Author validity detail: They discuss in their conclusion that the "lay population" they use (which is about 282 participants) should not be assumed to be representative of humanity as a whole. They also discuss the issue of using one language (English), as well as their efforts to validate the dataset through a panel of three moral philosophy experts. 
Taxonomy: Alignment / Alignment
New taxonomy: General Capability / Alignment