No detectable self-preference in LLMs accepting verified fixes to their own IFEval drafts versus neutral judges across four models and 85 comparisons (gap -5.1 pp, CI crossing zero).
arXiv preprint arXiv:2406.01297 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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FVRuleLearner retrieves learned operator-level reasoning rules to boost the functional correctness of LLM-generated SystemVerilog assertions by roughly 30 percentage points over simple prompting baselines.
SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.
citing papers explorer
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Self-Preference Is Weak or Absent in Verifiable Instruction-Following Revision: A Four-Model Test Under Genuine Authorship
No detectable self-preference in LLMs accepting verified fixes to their own IFEval drafts versus neutral judges across four models and 85 comparisons (gap -5.1 pp, CI crossing zero).
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FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification
FVRuleLearner retrieves learned operator-level reasoning rules to boost the functional correctness of LLM-generated SystemVerilog assertions by roughly 30 percentage points over simple prompting baselines.
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Training Language Models to Self-Correct via Reinforcement Learning
SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.