OLA: Output Language Alignment in Code-Switched LLM Interactions (2026 · 1 citation)
Multilingual users often mix languages in prompts without saying which language they want the answer in. Humans infer this easily, but LLMs often reply in the wrong language. The authors introduce OLA, a Korean–English benchmark with two settings:
FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation (2026)
When asked about sensitive topics, LLMs often give hedged, generic answers. They avoid harm but fail to address what the user actually asked. Existing evaluations judge such answers with coarse or subjective measures, so they offer little concrete guidance for fixing them. The authors propose FINEST, a fine-grained…
RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity (2026)
Role conflicts arise when obligations tied to two social roles, such as parent and employee, cannot both be met.

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Updated 1 November 2026