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Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 38% Moderate protocol signal Freshness: Warm Status: Ready
Pairwise Preference Automatic Metrics MathCoding
  • In the random-error setting, models strongly prefer correct completions in paired evaluation: 83.1% accuracy at balanced data and 67.0% even when correct rules appear in only 10% of the corpus.
  • Replacing random errors with a coherent but mathematically incorrect rule system largely eliminates the preference (near-chance accuracy).
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Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 35% Moderate protocol signal Freshness: Warm Status: Ready
Rubric Rating Llm As Judge Medicine
  • We introduce ThReadMed-QA, a benchmark of 2,437 fully-answered patient-physician conversation threads extracted from r/AskDocs, comprising 8,204 question-answer pairs across up to 9 turns.
  • We evaluate five state-of-the-art LLMs -- GPT-5, GPT-4o, Claude Haiku, Gemini 2.5 Flash, and Llama 3.3 70B -- on a stratified test split of 238 conversations (948 QA pairs) using a calibrated LLM-as-a-judge rubric grounded in physician…
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