Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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://bitbucket.org/tanya14109/cqasumm
공식 코드CQASUMM: Building References for Community Question Answering Summarization Corpora
https://bitbucket.org/tsendeemts/mnm
공식 코드https://bitbucket.org/tsendeemts/nse
공식 코드https://github.com/1094701018/ketm
공식 코드https://github.com/2015aroras/calmqa
공식 코드CaLMQA: Exploring culturally specific long-form question answering across 23 languages
https://github.com/21644h/DP_QA_joint_model
공식 코드https://github.com/2282588541a/hirag
공식 코드Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
https://github.com/3244we/question-rewriter
공식 코드Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter
https://github.com/3DAgentWorld/Toolkit-for-Prompt-Compression
공식 코드PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models
https://github.com/3b-group/convre
공식 코드An Investigation of LLMs' Inefficacy in Understanding Converse Relations
https://github.com/ABi-dot/Kedkg
공식 코드Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering
https://github.com/AFeng-x/Draw-and-Understand
공식 코드Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
https://github.com/AI-Application-and-Integration-Lab/Accept
공식 코드ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
https://github.com/AI-secure/InfoBERT
공식 코드InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
https://github.com/AI-secure/T3
공식 코드T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
https://github.com/AI21Labs/Parallel-Context-Windows
공식 코드https://github.com/AI4Bharat/MILU
공식 코드https://github.com/AI4Chem/ChemVlm
공식 코드ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
https://github.com/AIF4S/MediConfusion
공식 코드https://github.com/AIM-SKKU/QA-TIGER
공식 코드Question-Aware Gaussian Experts for Audio-Visual Question Answering
https://github.com/AIM3-RUC/YouMakeup_Baseline
공식 코드YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos
https://github.com/AIRI-Institute/knowledge-packing
공식 코드How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
https://github.com/AKSW/KG2Vec
공식 코드https://github.com/ATR-DBI/Map-EQA
공식 코드Map-based Modular Approach for Zero-shot Embodied Question Answering
https://github.com/AbhilashaRavichander/PrivacyQA_EMNLP
공식 코드Question Answering for Privacy Policies: Combining Computational and Legal Perspectives
https://github.com/Abhiram4572/Audiopedia
공식 코드https://github.com/Abhiram4572/mi_bart
공식 코드Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering
https://github.com/Abhishek0697/Detection-of-Hate-Speech-in-Multimodal-Memes
공식 코드https://github.com/Accelerator66/latent-gaussian-process-priors
공식 코드Raven's Progressive Matrices Completion with Latent Gaussian Process Priors
https://github.com/Agentic-Learning-AI-Lab/lifelong-memory
공식 코드LifelongMemory: Leveraging LLMs for Answering Queries in Long-form Egocentric Videos
https://github.com/AgnostiqHQ/multi-agent-llm
공식 코드Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
https://github.com/AhmedHussKhalifa/Dense_Passage_Retrieval_in_Conversational_Search
공식 코드https://github.com/AikyamLab/hallucinogen
공식 코드HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language Models
https://github.com/Aishwarya-NR/LRTA_Perturbed_Dataset
공식 코드https://github.com/AkariAsai/CORA
공식 코드One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.