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Implicatures

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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보존된 평가 기록

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코드

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

https://github.com/alexwarstadt/data_generation

공식 코드

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

https://github.com/fangru-lin/llm_scalar_adj

공식 코드

Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics

https://github.com/itl-ed/ns-arch

공식 코드

Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse

https://github.com/sjtu-compling/llm-pragmatics

공식 코드

Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.