A benchmark designed to measure whether language models produce truthful answers instead of repeating common misconceptions or misleading falsehoods.
BenchLM is tracking TruthfulQA in the local dataset, but exact-source verification records for these rows are still being attached. To avoid a blank benchmark page, BenchLM shows the current tracked rows below as a display-only reference table.
These tracked rows are useful for inspection and spot-checking, but until exact-source attachments are completed they should not be treated as fully verified public benchmark rows.
BenchLM mirrors the published tracked score view for TruthfulQA. Phi-4 leads the public snapshot at 77.5% , followed by Kimi K2.5 (57.3%). BenchLM does not use these results to rank models overall.
Year
2021
Tasks
Truthfulness and misconception resistance
Format
Question answering
Difficulty
Hallucination and factuality stress test
TruthfulQA matters because many models sound confident while repeating popular but false answers. It is a useful factuality and hallucination-adjacent benchmark even though it is older than newer factuality suites.
Version
TruthfulQA 2021
Refresh cadence
Static
Staleness state
Stale
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
A benchmark designed to measure whether language models produce truthful answers instead of repeating common misconceptions or misleading falsehoods.
Phi-4 currently leads the published TruthfulQA snapshot with a tracked score of 77.5%. BenchLM shows this benchmark for display only and does not use it in overall rankings.
2 AI models are included in BenchLM's mirrored TruthfulQA snapshot, based on the public leaderboard captured on April 8, 2026.
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