https://github.com/karthikshivasankar/text_classification
공식 코드TD-Suite: All Batteries Included Framework for Technical Debt Classification
Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/kartik21/xlingalignment
공식 코드Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs
https://github.com/katchu11/Robustness-of-MT-DNNs
공식 코드Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses
https://github.com/khanhtran0412/visomecens
공식 코드Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data
https://github.com/kimho666/llm_hardware_survey
공식 코드Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
https://github.com/kinjalbasu/explorer
공식 코드EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
https://github.com/kongds/mist
공식 코드https://github.com/kssteven418/I-BERT
공식 코드https://github.com/lamm-mit/Cephalo
공식 코드Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
https://github.com/lamm-mit/Cephalo-Phi-3-MoE
공식 코드Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
https://github.com/laohur/gur
공식 코드https://github.com/lautel/meg
공식 코드MEG: Medical Knowledge-Augmented Large Language Models for Question Answering
https://github.com/lcs2-iiitd/monteclora
공식 코드Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation
https://github.com/lemmonation/abnet
공식 코드Incorporating BERT into Parallel Sequence Decoding with Adapters
https://github.com/leo-yangli/vb-lora
공식 코드VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
https://github.com/leshanbog/PreSumm
공식 코드Advances of Transformer-Based Models for News Headline Generation
https://github.com/lexili24/NLUProject
공식 코드Meta-Learning for Natural Language Understanding under Continual Learning Framework
https://github.com/lexmen318/MultiWOZ-coref
공식 코드https://github.com/lexmen318/MultiWOZ_2.3
공식 코드https://github.com/lgw863/LogiQA-dataset
공식 코드LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
https://github.com/liangzid/MERGE
공식 코드https://github.com/lins-lab/capaboost
공식 코드Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
https://github.com/lksenel/coda21
공식 코드https://github.com/llyx97/sparse-and-robust-plm
공식 코드A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
https://github.com/loginaway/storyanalogy
공식 코드https://github.com/lorashen/auto-slurp
공식 코드Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant
https://github.com/lorenlugosch/end-to-end-SLU
공식 코드Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
https://github.com/luffy06/refusion
공식 코드https://github.com/luka-group/ddr
공식 코드Dense Retrieval as Indirect Supervision for Large-space Decision Making
https://github.com/luka-group/odda-data-augmentation
공식 코드On-the-fly Denoising for Data Augmentation in Natural Language Understanding
https://github.com/luka-group/pasta
공식 코드https://github.com/luka-group/read
공식 코드Robust Natural Language Understanding with Residual Attention Debiasing
https://github.com/luohongyin/entst
공식 코드https://github.com/luohongyin/langcode
공식 코드Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
https://github.com/macaronlin/llama3-quantization
공식 코드An empirical study of LLaMA3 quantization: from LLMs to MLLMs
https://github.com/machuangtao/llm-kg4qa
공식 코드Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.