ambiguity

This paper explores calibrated ambiguity as a generative resource in human communication versus multimodal language models, using the Dixit game to show that AI exhibits ambiguity collapse and lacks cultural references compared to humans.

This paper evaluates automated safety benchmarks for small language models, finding high ambiguity in judgments that compromises reliability and reveals a capability-safety confound.

This paper identifies a curse of ambiguity in language models, where more ambiguous next-token distributions are harder to learn, tracing this to architectural and learning roots and validating on synthetic and real data.

The article highlights that LLMs excel at ambiguous judgment tasks but are mediocre for consistent computation, advocating for task specialization in multi-agent systems.

A blog post examines an ambiguity in the C89 standard about implicit function declarations, where GCC and Clang disagree on interpretation, leading to different compile results for certain code.

This paper introduces CAPA, a benchmark for cross-session personalized ambiguity adaptation in coding assistants, characterizing six ambiguity mechanisms and evaluating 12 LLMs on 600 coding sessions with and without user history.

This blog post explores parsing ambiguities in C23 involving `auto` as a type inference specifier or storage-class specifier, showing how GCC and Clang disagree on parsing declarations like `auto x = 67;` when `x` is a typedef, and how attributes complicate the situation.

A discussion on the varying definitions of 'AI agents' and whether everyone means the same thing when using the term.

A discussion about tasks people think AI agents are ready for but aren't, highlighting the challenge of interpreting ambiguous human input like annoyed but unclear messages.

A developer describes the challenge of building multi-agent AI assistants that fail to handle unexpected situations gracefully, relying on explicit rules that lead to a whack-a-mole problem instead of enabling autonomous reasoning about ambiguity.

Introduces PRIG, a gradient attribution method that localizes prompt ambiguity in large language models by training a linear probe to distinguish clear from ambiguous prompts and attributing the probe score to token representations in the residual stream, achieving strong performance on synthetic and human-written benchmarks.