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VIDraft/vkae reacted to ginigen-ai's post with π₯ about 15 hours ago
π§ Does your LLM know when it's about to be wrong?
Most leaderboards measure accuracy. We measure metacognition β whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. π
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 β ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): β trap_rate β does it fall for tempting trap options? (lower = stronger) β‘ adapter gain Ξ β how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: π 300+100 trap problems (each with a hidden trap + TICOS type) π 24-model leaderboard 𧩠11 per-model adapters β adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state β P(wrong))
Submit any HF model β auto-scored daily at 09:00 KST and added to the board.
π Leaderboard β https://huggingface.co/spaces/ginigen-ai/Metacognition-Leaderboard-Space
π Benchmark β https://huggingface.co/datasets/ginigen-ai/Metacognition-Bench
𧩠Adapters β https://huggingface.co/collections/FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
π Article β https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai Β· Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech). reacted to ginigen-ai's post with β€οΈ about 15 hours ago
π§ Does your LLM know when it's about to be wrong?
Most leaderboards measure accuracy. We measure metacognition β whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. π
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 β ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): β trap_rate β does it fall for tempting trap options? (lower = stronger) β‘ adapter gain Ξ β how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: π 300+100 trap problems (each with a hidden trap + TICOS type) π 24-model leaderboard 𧩠11 per-model adapters β adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state β P(wrong))
Submit any HF model β auto-scored daily at 09:00 KST and added to the board.
π Leaderboard β https://huggingface.co/spaces/ginigen-ai/Metacognition-Leaderboard-Space
π Benchmark β https://huggingface.co/datasets/ginigen-ai/Metacognition-Bench
𧩠Adapters β https://huggingface.co/collections/FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
π Article β https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai Β· Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).Organizations
