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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
- 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
- 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
- KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by. . .
The probabilistic forecasting of time series is a well-recognized challenge, particularly in disentangling correlations among interacting time series and addressing the complexities of distribution modeling By treating time series as temporal dynamics, we introduce **KooNPro**, a novel probabilistic time series forecasting model that combines variance-aware deep **Koo**pman model with **N
- 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
- MIND over Body: Adaptive Thinking using Dynamic Computation
Clever use of intermediate activations to assess input complexity Should be able to work with existing architectures making engineering it for downstream real-world use cases simpler
- 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:
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