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Counterfactual Debiasing for Fact Verification 579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- ence stage In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information
Measuring Mathematical Problem Solving With the MATH Dataset Abstract: Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers To measure this ability in machine learning models, we introduce MATH, a new dataset of 12,500 challenging competition mathematics problems Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations
Weakly-Supervised Affordance Grounding Guided by Part-Level. . . In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric
NetMoE: Accelerating MoE Training through Dynamic Sample Placement 2 Clever design: reformulating the ILP to a weighted bipartite matching assignment problem and using Hungarian algorithm that has shorter solving time than communication time (so we can have actual speedup)
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION - OpenReview Abstract: Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques The first is the disentangled attention mechanism, where
Diffusion Generative Modeling for Spatially Resolved Gene. . . Weakness 3 (Novelty) The proposed method seems like a clever application of conditional diffusion models to the problem Can the authors further comment on the novelty of their method and how is it different compared to the existing literature? Thank you for allowing us to further clarify the novelty of Stem compared with existing methods
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 Our