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  • CLEVER: A Curated Benchmark for Formally Verified Code Generation
    TL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean It requires full formal specs and proofs No few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning
  • Forum - OpenReview
    The server responded with the following message: Too many requests: You have made 70 requests, surpassing the limit of 60 requests
  • Submissions - OpenReview
    Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers Lorenzo Pacchiardi , Marko Tesic , Lucy G Cheke , Jose Hernandez-Orallo 27 Sept 2024 (modified: 05 Feb 2025)
  • Counterfactual Debiasing for Fact Verification - OpenReview
    016 namely CLEVER, which is augmentation-free 017 and mitigates biases on the inference stage 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
  • Clever: A Curated Benchmark for Formally Verified Code Generation
    4CLEVER: Curated Lean Verified Code Generation Bench-mark microkernel However, writing formal specifications and correctness proofs for software systems can take tremen-dous effort — for example, the development of seL4 was reported to take 20+ person-years These costs are a key impediment to the broad deployment of ITP-based formal
  • Super Deep Contrastive Information Bottleneck for Multi . . . - OpenReview
    Super Deep Contrastive Information Bottleneck for Multi-modal Clustering Zhengzheng Lou 1Ke Zhang Yucong Wu Shizhe Hu1
  • LLaVA-OneVision: Easy Visual Task Transfer - OpenReview
    We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series
  • Initialization using Update Approximation is a Silver Bullet for. . .
    TL;DR: We provably optimally approximate full fine-tuning in low-rank subspaces throughout the entire training process using a clever initialization scheme, achieving significant gains in parameter efficiency




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