Papers with Code 아카이브

Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/justinphan3110/SciFive

공식 코드

SciFive: a text-to-text transformer model for biomedical literature

https://github.com/jvladika/ContextRAG

공식 코드

On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems

https://github.com/jvladika/improving-health-qa

공식 코드

Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval

https://github.com/jwallat/knowledge-acquisition

공식 코드

The Effect of Masking Strategies on Knowledge Retention by Language Models

https://github.com/jwallat/knowledge-probing

공식 코드

BERTnesia: Investigating the capture and forgetting of knowledge in BERT

https://github.com/jwoo5/ecg-qa

공식 코드

ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram

https://github.com/jxfeb/Generative_QA

공식 코드

Neural Generative Question Answering

https://github.com/jxliu-ai/medcot

공식 코드

MedCoT: Medical Chain of Thought via Hierarchical Expert

https://github.com/jyfang6/trace

공식 코드

TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation

https://github.com/jzbjyb/lm-calibration

공식 코드

How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering

https://github.com/jzbjyb/omnitab

공식 코드

OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering

https://github.com/jzbjyb/reatt

공식 코드

Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer

https://github.com/jzhoubu/hlp

공식 코드

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

https://github.com/jzhoubu/vdr

공식 코드

Semi-Parametric Retrieval via Binary Bag-of-Tokens Index

https://github.com/jzy-123/biod2c

공식 코드

BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA

https://github.com/kaavyarekanar/towards-a-performance-analysis-on-pre-trained-vqa-models-for-autonomous-driving

공식 코드

Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

https://github.com/kahnchana/mvu

공식 코드

Understanding Long Videos with Multimodal Language Models

https://github.com/kai-wen-yang/qvix

공식 코드

Good Questions Help Zero-Shot Image Reasoning

https://github.com/kaiserwholearns/consistenttokenization

공식 코드

Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks

https://github.com/kaist-lklab/cot-collection

공식 코드

The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaistai/cot-collection

공식 코드

The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kakaobrain/honeybee

공식 코드

Honeybee: Locality-enhanced Projector for Multimodal LLM

https://github.com/kakaobrain/stg

공식 코드

Selective Token Generation for Few-shot Natural Language Generation

https://github.com/kamfonas/GR-GRIM

공식 코드

What Can Secondary Predictions Tell Us? An Exploration on Question-Answering with SQuAD-v2.0

https://github.com/kangreen0210/lime

공식 코드

LIME: Less Is More for MLLM Evaluation

https://github.com/kangreen0210/lime-m

공식 코드

LIME: Less Is More for MLLM Evaluation

https://github.com/kartikeypant/text-simplification-qa-www2021

공식 코드

Text Simplification for Comprehension-based Question-Answering

https://github.com/karuna-bhaila/llm_unlearning

공식 코드

Soft Prompting for Unlearning in Large Language Models

https://github.com/kasuba-badri-vishal/dhrishtikon

공식 코드

DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images

https://github.com/katesanders9/grounded-events

공식 코드

A Survey of Video Datasets for Grounded Event Understanding

https://github.com/katherinewu312/lp-lm

공식 코드

LP-LM: No Hallucinations in Question Answering with Logic Programming

https://github.com/katiefraser/pairs

공식 코드

Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images

https://github.com/katzurik/implicitrelations

공식 코드

Inferring Implicit Relations in Complex Questions with Language Models

https://github.com/kaushal0494/UnifyingAITutorEvaluation

공식 코드

Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors

https://github.com/kayburns/tom-qa-dataset

공식 코드

Evaluating Theory of Mind in Question Answering

https://github.com/kazutoshishinoda/shortcutlearnability

공식 코드

Which Shortcut Solution Do Question Answering Models Prefer to Learn?

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