Polish Conversation Quality
Evaluating language models on multi-turn conversation quality in Polish across coding, extraction, humanities, math, reasoning, roleplay, STEM, and writing.
Polish Conversation Quality is a key task in natural language processing. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
Related Tasks
Question Answering
Question answering now spans extractive reading comprehension, open-domain retrieval QA, multi-hop reasoning, factuality, long-context QA, and web-browsing agents. SQuAD is historical; current QA evaluation needs Natural Questions, TriviaQA, HotpotQA, MuSiQue, DROP, KILT, SimpleQA, FRAMES, and BrowseComp.
Polish LLM General
General-purpose evaluation of language models on Polish language tasks: sentiment, reading comprehension, question answering, cyberbullying detection, and emotional intelligence.
Natural Language Inference
Determining entailment relationships between sentences (SNLI, MNLI).
Reading Comprehension
Understanding and answering questions about passages.
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