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20162025
most citedExplanations from Large Language Models Make Small Reasoners Better

35 citations · 153 across the 13 of their papers we have counts for

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22 papers · 1 filter

cs.CL202321 cited

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Xiang Yue, Xingwei Qu, Ge Zhang +5

We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on MathInstruct, o…

cs.CL2023

LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT

Le Zhuo, Ruibin Yuan, Jiahao Pan +10

We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets,…

cs.CL20234 cited

Few-shot In-context Learning for Knowledge Base Question Answering

Tianle Li, Xueguang Ma, Alex Zhuang +3

Question answering over knowledge bases is considered a difficult problem due to the challenge of generalizing to a wide variety of possible natural language questions. Additionall…

cs.CL2023

EDIS: Entity-Driven Image Search over Multimodal Web Content

Siqi Liu, Weixi Feng, Tsu-jui Fu +2

Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion…

cs.CL2023

TheoremQA: A Theorem-driven Question Answering dataset

Wenhu Chen, Ming Yin, Max Ku +6

The recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. However, their capabilities to s…

cs.CL20229 cited

MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text

Wenhu Chen, Hexiang Hu, Xi Chen +2

While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and even…