resource
Predictability and Surprise in Large Generative Models
Metadata
| Source Table | resources |
| Source ID | 370eb18a7329ea8b |
| Description | The paper identifies a core tension in large generative models: while their training loss follows predictable scaling laws, their specific capabilities, behaviors, and outputs remain difficult to anticipate. This unpredictability creates challenges for safe deployment and policy regulation. The auth… |
| Source URL | arxiv.org/abs/2202.07785 |
| Children | — |
| Created | Apr 10, 2026, 9:35 PM |
| Updated | Apr 10, 2026, 9:35 PM |
| Synced | Apr 10, 2026, 9:35 PM |
Record Data
id | 370eb18a7329ea8b |
url | arxiv.org/abs/2202.07785 |
title | Predictability and Surprise in Large Generative Models |
type | web |
summary | The paper identifies a core tension in large generative models: while their training loss follows predictable scaling laws, their specific capabilities, behaviors, and outputs remain difficult to anticipate. This unpredictability creates challenges for safe deployment and policy regulation. The auth… |
review | — |
abstract | — |
keyPoints | [ "Large generative models exhibit predictable aggregate loss (scaling laws) but unpredictable specific capabilities and outputs, creating a fundamental safety challenge.", "The appearance of useful, predictable capabilities drives rapid development, while unpredictable qualities make it hard to… |
publicationId | — |
authors | — |
authorEntityIds | — |
publishedDate | — |
tags | [ "capabilities", "deployment", "governance", "policy", "evaluation", "ai-safety", "alignment", "red-teaming", "technical-safety" ] |
localFilename | — |
credibilityOverride | — |
fetchedAt | — |
contentHash | — |
stableId | — |
fetchStatus | ok |
lastFetchedAt | Apr 10, 2026, 9:35 PM |
archiveUrl | — |
stance | — |
contextNote | This Anthropic paper examines the paradox of large generative models having predictable scaling laws but unpredictable emergent capabilities, with direct implications for AI safety governance and deployment risk assessment. |
resourcePurpose | analysis |
resourceSubtype | conference_paper |
typeMetadata | — |
publisherEntityId | — |
relatedEntityIds | — |
enrichmentStatus | enriched |
enrichmentDate | Apr 10, 2026, 9:35 PM |
importanceScore | 0.72 |
contentLifecycle | — |
Debug info
Thing ID: 370eb18a7329ea8b
Source Table: resources
Source ID: 370eb18a7329ea8b