https://github.com/li-group/optichat
Diagnosing Infeasible Optimization Problems Using Large Language Models
Papers with Code 아카이브
Chatbot
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Chatbot or conversational AI is a language model designed and implemented to have conversations with humans.
Source: Open Data Chatbot
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lightblue-tech/multilingual-mt-bench
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/liliaellouz/wabi-sabi
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow
https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
https://github.com/lkfo415579/MT-Readling-List
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/llm-council/llm-council
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
https://github.com/lm-sys/arena-hard
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
https://github.com/lm-sys/routellm
https://github.com/lmarena/ppe
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
https://github.com/lonePatient/knowledge-driven-dialogue-lic2019-rank5
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/lucylow/En_francais_si_vous_plait-
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/luogen1996/lavin
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
https://github.com/lupantech/MathVista
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
https://github.com/mangopy/direct-rag-learning
Wizard of Wikipedia: Knowledge-Powered Conversational agents
https://github.com/markWJJ/TC-Bot
https://github.com/martinambition/IntentClassificationBenchmark
Subword Semantic Hashing for Intent Classification on Small Datasets
https://github.com/maszhongming/unieval
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/mayurnewase/Translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mcgill-nlp/faithdial
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
https://github.com/megagonlabs/llm-longeval
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
https://github.com/mgmalek/efficient_cross_entropy
https://github.com/microsoft/bang
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
https://github.com/mike-a-yen/date-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mindspore-ai/models/tree/master/official/nlp/pangu_alpha
https://github.com/mindspore-courses/DeepNLP-models-MindSpore
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/misonsky/multilingualdatasets
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
https://github.com/moon23k/Attention_Anchors
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mp2893/gram
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mpavlovic/insincere-questions-classifier
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/murtrax/Self-Feeding-Chatbot-With-Context-Aware-Questions
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
https://github.com/nawnoes/pytorch-meena
https://github.com/neqkir/attention-mechanism
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ninglab/ecellm
https://github.com/nonslowrunner/Visualdialogs
https://github.com/noriyukipy/gptchat
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.