1markforaspartialorlilexnation;2foraclearexnation,whichappreciates theunderstatement[2] ©CambridgeInternationalExaminations2015 Page3MarkSchemeSyllabusPaper CambridgeIGCSE–October/November2015050011 (d)Usingyourownwords,exintheeffectofthesentence,‘Waterswarmedinfromall ...
The official CLIP training codebase of Inf-CL: "Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss". A super memory-efficiency CLIP training scheme. reasoning-paths-optimizationPublic [EMNLP 2024] Reasoning Paths Optimization: Learning to Reason and Explore From Diverse...
One of the fundamental questions about human language is whether all languages are equally complex. Here, we approach this question from an information-theoretic perspective. We present a large scale quantitative cross-linguistic analysis of written lang
[arxiv] Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs.2024.01 [arxiv] Clue-Guided Path Exploration: An Efficient Knowledge Base Question-Answering Framework with Low Computational Resource Consumption.2024.01 [AAAI 2024] KAM-CoT: Knowledge Augmented Mult...
DiVeRSe has three main components: first, it generates diverse prompts to explore different reasoning paths for the same question; second, it uses a verifier to filter out incorrect answers based on a weighted voting scheme; and third, it verifies each reasoning step individually instead of the ...
以下是来自于9868 文学卷Paper 3的mark scheme的英文版本的个人中文翻译,解释权归CAIE,结合我改卷的经验以及大纲的原文进行分析。 Passage based questions: Examiners should consider the extent to which candidates have been able to identify the significant issues raised in the passage and,where appropriate,...
They use the GPT-2 model to combine with the BERT model. This combination makes better use of collaborative question generation and question answering. In paper [17], they use RoBERTa as the main model. But at the same time, additional CRF layers were added, and training was performed on ...
The codon model thus demonstrates superior performance across various unrelated tasks, and against a variety of benchmarks. We then considered the question of whether the improvement in prediction is due to synonymous codon usage. If patterns of codon usage contain valuable information, then ...
enabling the LLM to access a rich tapestry of visual knowledge. The training methodology of BLIVA is structured around a two-stage scheme: initial pre-training on image-text pairs derived from captioning datasets, followed by instruction tuning using Visual Question Answering (VQA) data. This proc...
Automated Story Generation as Question-Answering. Louis Castricato, Spencer Frazier, Jonathan Balloch, Nitya Tarakad, Mark Riedl. [pdf] AAAI 2022 DCT: Dynamic Compressive Transformer for Modeling Unbounded Sequence. Kai-Po Chang, Wei-Yun Ma. [pdf] arxiv 2021 Hierarchical Transformers Are More Eff...