https://github.com/i3omar/ForestQB
공식 코드Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models
Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/i3omar/LLM-Integration-Data
공식 코드Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models
https://github.com/iacercalixto/visualsem
공식 코드VisualSem: A High-quality Knowledge Graph for Vision and Language
https://github.com/idankdev/helu
공식 코드https://github.com/idea-ccnl/fengshenbang-lm
공식 코드Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
https://github.com/idiap/atco2-corpus
공식 코드https://github.com/idiap/hypermixing
공식 코드HyperMixer: An MLP-based Low Cost Alternative to Transformers
https://github.com/indobenchmark/indonlu
공식 코드IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
https://github.com/indonlp/nusa-crowd
공식 코드NusaCrowd: Open Source Initiative for Indonesian NLP Resources
https://github.com/intelligent-vehicles/bevdriver
공식 코드BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving
https://github.com/jabhinav/prompt-tuning-strikes-back-with-lopa
공식 코드Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
https://github.com/jacobkrantz/lstm-syllabify
공식 코드Language-Agnostic Syllabification with Neural Sequence Labeling
https://github.com/jahid006/gan-bnbert
공식 코드Bengali Intent Classification with Generative Adversarial BERT
https://github.com/jasonwu0731/ToD-BERT
공식 코드TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue
https://github.com/jasonzy121/TextGNN
공식 코드TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
https://github.com/jdeschena/sdtt
공식 코드Beyond Autoregression: Fast LLMs via Self-Distillation Through Time
https://github.com/jej127/bias-experts
공식 코드Improving Bias Mitigation through Bias Experts in Natural Language Understanding
https://github.com/jfchi/plue
공식 코드PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English
https://github.com/jin8/adlm
공식 코드Language Detoxification with Attribute-Discriminative Latent Space
https://github.com/jitinkrishnan/Multilingual-ZeroShot-SlotFilling
공식 코드Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling
https://github.com/jl-er/bone
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jl-er/disha
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/joestacey/atomic_inference_anli
공식 코드https://github.com/johnnay/llm-lobbyist
공식 코드https://github.com/johnsonjiang1996/awesome-vla4ad
공식 코드A Survey on Vision-Language-Action Models for Autonomous Driving
https://github.com/jonathanqzheng/neo-bench
공식 코드NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms
https://github.com/joonkeekim/Instructive-Decoding
공식 코드https://github.com/joshinh/investigation-cad
공식 코드An Investigation of the (In)effectiveness of Counterfactually Augmented Data
https://github.com/jstarc/nli_generation
공식 코드Constructing a Natural Language Inference Dataset using Generative Neural Networks
https://github.com/junhua/EPIC
공식 코드EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets
https://github.com/juyongjiang/kasa
공식 코드KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
https://github.com/juzhengz/lori
공식 코드LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation
https://github.com/jwallat/temporalblindspots
공식 코드https://github.com/kakaobrain/KorNLUDatasets
공식 코드KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
https://github.com/kakaobrain/kortok
공식 코드An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks
https://github.com/kandorm/CLINE
공식 코드CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.