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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
- 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
- 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
- Faster Cascades via Speculative Decoding | OpenReview
Cascades and speculative decoding are two common approaches to improving language models' inference efficiency Both approaches interleave two models, but via fundamentally distinct mechanisms:
- 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
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