https://github.com/nico-koltermann/contextual-evolution-of-heuristics
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems
Papers with Code 아카이브
In-Context Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nju-websoft/FBPrompt
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
https://github.com/njunlp/mmt-llm
https://github.com/opennlg/openba-v2
https://github.com/owennju/mmt-llm
https://github.com/parkervg/blendsql
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
https://github.com/phunterlau/paper_without_code/tree/main/examples/InCA
In-context Continual Learning Assisted by an External Continual Learner
https://github.com/plachtaa/vall-e-x
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/prapti19/prompt_autohint
AutoHint: Automatic Prompt Optimization with Hint Generation
https://github.com/protllm/protllm
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
https://github.com/pwc-1/Paper-10/tree/main/seggpt
https://github.com/rabiulcste/vqazero
Kosmos-2: Grounding Multimodal Large Language Models to the World
https://github.com/raldir/admiral
Learning to Generate Answers with Citations via Factual Consistency Models
https://github.com/re-align/urial
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
https://github.com/requeima/llm_processes
JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
https://github.com/ruc-nlpir/flashrag
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
https://github.com/sachith-gunasekara/t4d
How FaR Are Large Language Models From Agents with Theory-of-Mind?
https://github.com/safolab-wisc/dolphins
https://github.com/safolab-wisc/fiubench
https://github.com/sambanova/generative_data_prep
https://github.com/sebhaan/TabPFGen
https://github.com/serp-ai/bark-with-voice-clone
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/shaoyangxu/culturespa
Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning
https://github.com/silviatti/prompt-based-user-simulator
In-Context Learning User Simulators for Task-Oriented Dialog Systems
https://github.com/skzhang1/ideal
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
https://github.com/social-ai-studio/pro-cap
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations
https://github.com/stanford-oval/wikidata-emnlp23
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
https://github.com/stanfordnlp/dspy
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
https://github.com/stevekgyang/mentallama
Towards Interpretable Mental Health Analysis with Large Language Models
https://github.com/suno-ai/bark
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/sunzeyeah/rlhf
https://github.com/suzgunmirac/meta-prompting
https://github.com/sweta20/incontextmt
https://github.com/tabpfn-anonym/tabpfnanonym
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/tanyuqian/cappy
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer
https://github.com/teacherpeterpan/programfc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.