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Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

코드

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

https://github.com/cloudmcloudyo/capstone

Attention Is All You Need

https://github.com/clovaai/textual-kd-slu

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/clvrai/relation-network-tensorflow

A simple neural network module for relational reasoning

https://github.com/cma1114/activation_steering

Representation Engineering: A Top-Down Approach to AI Transparency

https://github.com/cmbagent/cmbagent

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

https://github.com/cmbagents/cmbagent

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

https://github.com/cmsflash/efficient-attention

Attention Is All You Need

https://github.com/cmunnis/BERT_vs_Transformer-XL

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/cnichkawde/MatchingNetwork

A simple neural network module for relational reasoning

https://github.com/coastalcph/eu-politics-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/coastalcph/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

https://github.com/coco60/bert-test

Attention Is All You Need

https://github.com/code-implementation1/Code7/tree/main/SDNet

SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering

https://github.com/codedecde/Recognizing-Textual-Entailment

Recurrent Batch Normalization

https://github.com/codedotal/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/codegram/calbert

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/codelion/optillm

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

https://github.com/codelion/optillm/blob/main/optillm/entropy_decoding.py

Entropy-Based Decoding for Retrieval-Augmented Large Language Models

https://github.com/codelion/optillm/blob/main/optillm/leap.py

In-Context Principle Learning from Mistakes

https://github.com/coderalo/QACNN-PyTorch

Query-based Attention CNN for Text Similarity Map

https://github.com/codertimo/BERT-pytorch

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/codykala/ABCNN

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

https://github.com/cognitiveailab/questionclassification

Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

https://github.com/colorfulscoop/tfdlg

Attention Is All You Need

https://github.com/columbia-ai-robotics/semantic-abstraction

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

https://github.com/common-english/bert-all

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/conceptofmind/LaMDA-pytorch

Attention Is All You Need

https://github.com/conceptofmind/PaLM-flax

PaLM: Scaling Language Modeling with Pathways

https://github.com/conceptofmind/lamda-rlhf-pytorch

Attention Is All You Need

https://github.com/congyingxia/ZeroShotCapsule

Zero-shot User Intent Detection via Capsule Neural Networks

https://github.com/consequentai/fneval

Let's Verify Step by Step

https://github.com/contextlab/abstract2paper

Language Models are Few-Shot Learners

https://github.com/cooelf/SG-Net

SG-Net: Syntax-Guided Machine Reading Comprehension

https://github.com/cooijmanstim/recurrent-batch-normalization

Recurrent Batch Normalization

https://github.com/coronazap/bert_client

Attention Is All You Need

https://github.com/cosmoquester/2021-dialogue-summary-competition

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

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