|
- Counterfactual Debiasing for Fact Verification - OpenReview
016 namely CLEVER, which is augmentation-free 017 and mitigates biases on the inference stage 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
- Measuring Mathematical Problem Solving With the MATH Dataset
To find the limits of Transformers, we collected 12,500 math problems While a three-time IMO gold medalist got 90%, GPT-3 models got ~5%, with accuracy increasing slowly
- Progressive Growing of GANs for Improved Quality, Stability, and . . .
The test is a clever idea but it's also highly subjective and thus quite difficult to use in practice Camera ready version Official Comment by Paper447 Authors • Camera ready version
- NetMoE: Accelerating MoE Training through Dynamic Sample Placement
Mixture of Experts (MoE) is a widely used technique to expand model sizes for better model quality while maintaining the computation cost constant
- LoRA: Low-Rank Adaptation of Large Language Models
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains As we pre-train larger models, full
- Learning when to Communicate at Scale in Multiagent Cooperative and . . .
Abstract: Learning when to communicate and doing that effectively is essential in multi-agent tasks Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks
- LLMOPT: Learning to Define and Solve General Optimization Problems. . .
Optimization problems are prevalent across various scenarios Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise, which could block the widespread application of optimization-based decision making
- Least-to-Most Prompting Enables Complex Reasoning in Large Language . . .
We propose a novel prompting strategy, least-to-most prompting, that enables large language models to achieve easy-to-hard generalization
|
|
|