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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
- 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
- Reasoning of Large Language Models over Knowledge Graphs with. . .
While large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate This limitation reduces
- Thieves on Sesame Street! Model Extraction of BERT-based APIs
Outputs of modern NLP APIs on nonsensical text provide strong signals about model internals, allowing adversaries to steal the APIs
- ALBERT: A L BERT FOR SELF SUPERVISED LEARNING OF L . . . - OpenReview
Existing solutions to the aforementioned problems include model parallelization (Shazeer et al , 2018; Shoeybi et al , 2019) and clever memory management (Chen et al , 2016; Gomez et al , 2017)
- Progressive Growing of GANs for Improved Quality, Stability, and. . .
We train generative adversarial networks in a progressive fashion, enabling us to generate high-resolution images with high quality
- ICLR 2025 Workshop LLM Reason and Plan | OpenReview
Workshop on Reasoning and Planning for Large Language Models Reasoning and Planning for LLMs @ ICLR2025 Singapore Apr 27 2025 https: workshop-llm-reasoning-planning github io zhiyuanhucs@gmail com
- 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
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