arXiv:2606.05486v1 Announce Type: new Abstract: Prompt ambiguity is a common source of failure in large language models, but is difficult to localize because it is a latent property of the prompt, while existing attribution methods are designed to explain observable outputs such as logits or generated tokens. We introduce PRIG, a gradient attribution method that uses a probe logit to attribute latent ambiguity to token positions. Specifically, PRIG trains a linear probe to distinguish clear prompts from ambiguous prompts and attributes the probe score to earlier token representations in the re
The rapid deployment and increasing complexity of large language models are highlighting the critical but elusive problem of prompt ambiguity, making tools to address it highly relevant now.
Understanding and localizing prompt ambiguity is crucial for improving the reliability, safety, and performance of AI systems, particularly as they are integrated into sensitive applications.
The introduction of PRIG provides a novel method for identifying and attributing latent prompt ambiguity, enabling more targeted prompt engineering and model refinement beyond observable outputs.
- · AI researchers
- · Large Language Model developers
- · AI safety and ethics organizations
- · Enterprises deploying LLMs
- · Developers ignoring prompt ambiguity
- · Black box AI solutions
Improved debugging and fine-tuning of large language models, leading to more robust AI applications.
New best practices in prompt engineering emerge, emphasizing the detection and mitigation of ambiguity during development.
The development of 'ambiguity-aware' LLMs that can internally identify and potentially resolve ambiguous prompts, enhancing interaction quality.
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