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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
- Large Language Models are Human-Level Prompt Engineers
We propose an algorithm for automatic instruction generation and selection for large language models with human level performance
- 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
- 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
- Eureka: Human-Level Reward Design via Coding Large Language Models
Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem We bridge this fundamental gap and present Eureka, a human-level reward design algorithm powered by LLMs Eureka exploits the remarkable zero-shot
- Probabilistic Learning to Defer: Handling Missing Expert. . .
The authors propose a formulation that relies on a clever application of the expectation-maximization algorithm, which naturally handles missing data Additionally, they introduce a constraint within the expectation stage of the algorithm to manage expert workloads
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