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Natural Language Understanding

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하위 태스크: 2
평가표: 6
보존된 평가 기록: 42

Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.

Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/magiasn/acl2024_rllr

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Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding

https://github.com/magiccircuit/treemix

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TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding

https://github.com/mahadi-nahid/tabsqlify

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TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition

https://github.com/mahesh-ak/SktQA

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A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs

https://github.com/mainlp/auxtasks-bavarian-sid

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Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study

https://github.com/mainlp/dialect-BLI

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Low-resource Bilingual Dialect Lexicon Induction with Large Language Models

https://github.com/mainlp/norsid

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Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detection

https://github.com/marekkubis/bteval

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Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors

https://github.com/mario-math-reasoning/mario

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MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline

https://github.com/mariomeissner/shortcut-pruning

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Debiasing Masks: A New Framework for Shortcut Mitigation in NLU

https://github.com/mateuszmalinowski/visual_turing_test-tutorial

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Tutorial on Answering Questions about Images with Deep Learning

https://github.com/mayukaj/llm-boost

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Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes

https://github.com/mcGill-NLP/medal

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MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

https://github.com/mediabrain-sjtu/ming

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MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

https://github.com/mega002/qdmr-based-question-generation

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Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition

https://github.com/mehdimojarradi/Ensemble-SuperICL

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Improving In-Context Learning with Small Language Model Ensembles

https://github.com/meryemmhamdi1/meta_cross_nlu_qa

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X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering

https://github.com/meta-math/MetaMath

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MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

https://github.com/microsoft/DeBERTa

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DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/microsoft/Distilled-Sentence-Embedding

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Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding

https://github.com/microsoft/PythonProgrammingPuzzles

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Programming Puzzles

https://github.com/microsoft/TextGNN

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TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search

https://github.com/microsoft/clues

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CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

https://github.com/microsoft/deep-language-networks

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Joint Prompt Optimization of Stacked LLMs using Variational Inference

https://github.com/microsoft/efficientlongsequencemodeling

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Efficient Long Sequence Modeling via State Space Augmented Transformer

https://github.com/microsoft/fastformers

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FastFormers: Highly Efficient Transformer Models for Natural Language Understanding

https://github.com/microsoft/glge

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GLGE: A New General Language Generation Evaluation Benchmark

https://github.com/microsoft/moonlit

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Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models

https://github.com/microsoft/taskweaver

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TaskWeaver: A Code-First Agent Framework

https://github.com/microsoft/unilm

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Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/microsoft/unilm/tree/master/deltalm

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DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders

https://github.com/midas-research/calling-out-bluff

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Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems

https://github.com/midas-research/interpreting-AES-Integrated-Gradients

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My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism

https://github.com/mihir3009/multi-logieval

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Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models

https://github.com/milliemaoo/prompt-position

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Do prompt positions really matter?

https://github.com/mingliiii/reflection_tuning

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Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning

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