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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)
- Training Large Language Model to Reason in a Continuous Latent Space
Large language models are restricted to reason in the “language space”, where they typically express the reasoning process with a chain-of-thoughts (CoT) to solve a complex reasoning problem
- LLMOPT: Learning to Define and Solve General Optimization Problems. . .
Given the simplicity of the problems in this dataset, (1) this difference is unlikely to be explained by clever reformulations that improve solution time, (2) and unlikely to be noise
- 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
- 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
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