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/harsh19/Reasoning-Chains-MultihopQA

공식 코드

Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering

https://github.com/harshraj22/vqa

공식 코드

Multi-Image Visual Question Answering

https://github.com/harvardnlp/encoder-agnostic-adaptation

공식 코드

Encoder-Agnostic Adaptation for Conditional Language Generation

https://github.com/harvardnlp/var-attn

공식 코드

Latent Alignment and Variational Attention

https://github.com/hashmatshadab/robust-llava

공식 코드

Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models

https://github.com/hazel-heejeong-nam/vague

공식 코드

Visual Contexts Clarify Ambiguous Expressions: A Benchmark Dataset

https://github.com/hazyresearch/ama_prompting

공식 코드

Ask Me Anything: A simple strategy for prompting language models

https://github.com/hazyresearch/h3

공식 코드

Hungry Hungry Hippos: Towards Language Modeling with State Space Models

https://github.com/hazyresearch/safari

공식 코드

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://github.com/hazyresearch/tabi

공식 코드

TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

https://github.com/hchoi256/i-halla-v1.0

공식 코드

Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering

https://github.com/hcplab-sysu/causalvlr

공식 코드

MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments

https://github.com/hcplab-sysu/cmcir

공식 코드

Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering

https://github.com/hcy123902/atg-w-fg-rw

공식 코드

Training Language Models to Generate Text with Citations via Fine-grained Rewards

https://github.com/healthylaife/faircdsllm

공식 코드

Bias patterns in the application of LLMs for clinical decision support: A comprehensive study

https://github.com/heendung/ii-mmr

공식 코드

II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering

https://github.com/heendung/simpleaug

공식 코드

Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering

https://github.com/hejunqing/never-lost-in-the-middle

공식 코드

Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training

https://github.com/heliossun/sq-llava

공식 코드

SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant

https://github.com/heliossun/stllava-med

공식 코드

STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering

https://github.com/helloeve/bert-co-matching

공식 코드

Do Multi-hop Readers Dream of Reasoning Chains?

https://github.com/hemile/iswc-challenge

공식 코드

Prompting as Probing: Using Language Models for Knowledge Base Construction

https://github.com/hendrycks/math

공식 코드

Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/hendryx-scale/mhal-detect

공식 코드

Detecting and Preventing Hallucinations in Large Vision Language Models

https://github.com/hengrui-gu/pokemqa

공식 코드

PokeMQA: Programmable knowledge editing for Multi-hop Question Answering

https://github.com/henryzhao5852/BeamDR

공식 코드

Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

https://github.com/henryzhao5852/distdr

공식 코드

Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation

https://github.com/heraclex12/NLP2SPARQL

공식 코드

SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs

https://github.com/hessaalawwad/plr-tqa

공식 코드

Enhancing textual textbook question answering with large language models and retrieval augmented generation

https://github.com/heydaari/CAG

공식 코드

Context Awareness Gate For Retrieval Augmented Generation

https://github.com/heydarsoudani/ragvsft

공식 코드

Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

https://github.com/heyjoonkim/apa

공식 코드

Aligning Language Models to Explicitly Handle Ambiguity

https://github.com/heyunh2015/diseaseBERT

공식 코드

Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition

https://github.com/hfmei/veattack-lvlm

공식 코드

VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models

https://github.com/hfutml/calibration-mllm

공식 코드

Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models

https://github.com/hhy-huang/HiRAG

공식 코드

Retrieval-Augmented Generation with Hierarchical Knowledge

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