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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 …
Measuring Mathematical Problem Solving With the MATH Dataset
Oct 18, 2021 · 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.
Alias-Free Mamba Neural Operator - OpenReview
Sep 25, 2024 · Functionally, MambaNO achieves a clever balance between global integration, facilitated by state space model of Mamba that scans the entire function, and local integration, …
The Predictron: End-To-End Learning and Planning - OpenReview
Nov 4, 2016 · One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. …
Ignore Previous Prompt: Attack Techniques For Language Models Fábio Perez Ian Ribeiro AE Studio {fperez,ian.ribeiro}@ae.studio
A Deep Reinforced Model for Abstractive Summarization
Feb 15, 2018 · A summarization model combining a new intra-attention and reinforcement learning method to increase summary ROUGE scores and quality for long sequences.
Reconstructive Visual Instruction Tuning - OpenReview
Jan 22, 2025 · This paper introduces reconstructive visual instruction tuning (ROSS), a family of Large Multimodal Models (LMMs) that exploit vision-centric supervision signals.
Understanding deep learning requires rethinking generalization
Through extensive systematic experiments, we show how the traditional approaches fail to explain why large neural networks generalize well in practice, and why understanding deep learning …
ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box...
Jan 22, 2025 · TL;DR: We propose Zeroth-order Intrinsic-dimensional Prompt-tuning (ZIP), a method that reduces query demands in black-box prompt-tuning by optimizing in a lower …
LoRA: Low-Rank Adaptation of Large Language Models
Jan 28, 2022 · 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 …
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