Shared Lexical Task Representations Explain Behavioral Variability In LLMs
One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the question is posed. We investigate this variation by comparing two very different but commonly-used styles of prompting: instruction-based prompts, which describe the task in natural language, and example-based prompts, which provide in-context few-shot demonstration pairs to illustrate the task. We find that, despite large variation in performance as a function of the prompt, the model engages some common underlying mechanisms across different prompts of a...
| Authors | Zhuonan Yang, Jacob Xiaochen Li, Francisco Piedrahita Velez et al. |
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| Primary category | cs.CL |
| Published | 23 April 2026 |
| Last updated at source | 29 June 2026 |
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