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[2509.02452] Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
Summary
This article investigates whether large language models (LLMs) adhere to external label definitions or rely on internal representations, revealing mixed results across various tasks.
Why It Matters
Understanding how LLMs process external definitions is crucial for improving their accuracy and explainability. This research highlights the inconsistency in LLMs' integration of external knowledge, which has implications for their application in both general and domain-specific tasks.
Key Takeaways
- LLMs often default to internal representations over external definitions.
- Explicit label definitions can enhance accuracy, especially in domain-specific tasks.
- The integration of external definitions into LLMs is not guaranteed or consistent.
- Controlled experiments reveal varying receptivity to label definitions.
- A deeper understanding of LLMs' processing of external knowledge is needed.
Computer Science > Computation and Language arXiv:2509.02452 (cs) [Submitted on 2 Sep 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions Authors:Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba, Edward Raff, Ponnurangam Kumaraguru, Francis Ferraro, Manas Gaur View a PDF of the paper titled Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions, by Seyedali Mohammadi and 6 other authors View PDF HTML (experimental) Abstract:Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM-generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM's task-solving processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities....