Evaluating Ill-Defined Tasks in Large Language Models
This work highlights critical flaws in LLM evaluation practices for ill-defined tasks, which is important for researchers and practitioners seeking reliable model assessments, though it is incremental as it builds on existing critique without introducing a new method.
The paper analyzed why current evaluation benchmarks and metrics fail to reliably assess Large Language Models (LLMs) on ill-defined tasks, using case studies like Complex Instruction Following and Natural Language to Mermaid Sequence Diagrams to show that these evaluations often produce unstable and non-diagnostic scores.
Many evaluations of Large Language Models (LLMs) target tasks that are inherently ill-defined, with unclear input and output spaces and ambiguous success criteria. We analyze why existing evaluation benchmarks and metrics fail to provide reliable or diagnostic signals of model capability for such tasks. We examine two case studies: Complex Instruction Following (CIF), where we identify recurring issues including limited coverage of real-world instruction complexity, sensitivity to instruction phrasing, inconsistent and non-comparable metrics, and instability introduced by LLM-based judges; and Natural Language to Mermaid Sequence Diagrams (NL2Mermaid), where we show how multi-faceted evaluation criteria can yield actionable insights beyond aggregate scores. Together, these case studies show that current evaluations frequently conflate distinct failure modes, yielding scores that are unstable, non-diagnostic, and difficult to act upon. Our findings expose fundamental limitations in existing evaluation practices for ill-defined tasks and motivate more robust, interpretable evaluation designs.