Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/curai/curai-research
공식 코드DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
https://github.com/cyp-jlu-ai/ba-lora
공식 코드https://github.com/czhang99/Capsule-NLU
공식 코드Joint Slot Filling and Intent Detection via Capsule Neural Networks
https://github.com/dadelani/sib-200
공식 코드https://github.com/damithdr/questionanswering
공식 코드https://github.com/damo-nlp-sg/pmr
공식 코드https://github.com/danielhers/hit-scir-ucca-parser
공식 코드Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
https://github.com/danielsun94/conversational_diagnosis
공식 코드Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
https://github.com/danifg/disco-seq2seq-parser
공식 코드https://github.com/dccuchile/lightweight-spanish-language-models
공식 코드https://github.com/declare-lab/knot
공식 코드KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
https://github.com/declare-lab/sinkhorn-loss
공식 코드KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
https://github.com/declare-lab/vip
공식 코드Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding
https://github.com/deqingfu/scene
공식 코드SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples
https://github.com/diego-feijo/bertpt
공식 코드Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks
https://github.com/dinghanshen/Cutoff
공식 코드https://github.com/dipta007/semeval24-task8
공식 코드https://github.com/dj1121/tlm_num_probe
공식 코드Probing for Multilingual Numerical Understanding in Transformer-Based Language Models
https://github.com/dmg-illc/prime-lm
공식 코드https://github.com/dong-river/llm_unlearning
공식 코드UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models
https://github.com/dreasysnail/INSET
공식 코드https://github.com/drsy/pins
공식 코드Pruning Pre-trained Language Models with Principled Importance and Self-regularization
https://github.com/duygua/semeval2024_nli4ct
공식 코드D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities of Large Language Models
https://github.com/eduard6421/ronli
공식 코드https://github.com/edwardgowsmith/improved-tokenisation-methods
공식 코드https://github.com/efficient-ml/qwen3-quantization
공식 코드https://github.com/ekaterinavoloshina/chronological_probing
공식 코드Is neural language acquisition similar to natural? A chronological probing study
https://github.com/eleutherai/pythia
공식 코드https://github.com/emilygoodwin/systematicity
공식 코드https://github.com/emory-irlab/CDAC
공식 코드Contextual Dialogue Act Classification for Open-Domain Conversational Agents
https://github.com/emunatool/BARcode-BioInspired-Search
공식 코드Imitation of Life: A Search Engine for Biologically Inspired Design
https://github.com/eric11eca/inference-information-probing
공식 코드Probing Linguistic Information For Logical Inference In Pre-trained Language Models
https://github.com/ericlee8/biden
공식 코드Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling
https://github.com/ernie-research/gptfluence
공식 코드https://github.com/eternityyw/metacognitive-prompting
공식 코드Metacognitive Prompting Improves Understanding in Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.