https://github.com/blitzprecision/ficle
공식 코드Neural models for Factual Inconsistency Classification with Explanations
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://github.com/bloomberg/kbir_keybart
공식 코드https://github.com/bmi-labmedinfo/nlp-radiology-paper
공식 코드Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers
https://github.com/bnmin/tdp_ranking
공식 코드Exploring Contextualized Neural Language Models for Temporal Dependency Parsing
https://github.com/boheumd/MA-LMM
공식 코드MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
https://github.com/booydar/babilong
공식 코드BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
https://github.com/boru-roylu/encode-once-and-decode-in-parallel
공식 코드Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks
https://github.com/boschresearch/f1-is-not-enough
공식 코드F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
https://github.com/boschresearch/freb-tqa
공식 코드FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering
https://github.com/boschresearch/fres
공식 코드https://github.com/boun-tabi/RELX
공식 코드The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification
https://github.com/boun-tabi/squad-tr
공식 코드Building Efficient and Effective OpenQA Systems for Low-Resource Languages
https://github.com/bowang-rw-02/scl-one-for-four
공식 코드One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning
https://github.com/bowen-upenn/Multi-Agent-VQA
공식 코드Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
https://github.com/bozheng-hit/xTune
공식 코드https://github.com/brandonman123/videocad
공식 코드https://github.com/braqiiit/qrealboost
공식 코드https://github.com/briansidp/audiovisualllm
공식 코드Fine-grained Audio-Visual Joint Representations for Multimodal Large Language Models
https://github.com/bright2013/CorefAwareMRC
공식 코드Tracing Origins: Coreference-aware Machine Reading Comprehension
https://github.com/brihijoshi/granular-similarity-COLING-2020
공식 코드The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks
https://github.com/brown-palm/Vamos
공식 코드https://github.com/brownfortress/llms-trc
공식 코드Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?
https://github.com/bruthyu/melo
공식 코드MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
https://github.com/bsantraigi/HIER
공식 코드https://github.com/bsantraigi/hier-transformer-pytorch
공식 코드https://github.com/btzyd/qava
공식 코드QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models
https://github.com/buaadreamer/easyrag
공식 코드EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
https://github.com/bugtig6351/lfqa_evaluation
공식 코드An Empirical Study of Evaluating Long-form Question Answering
https://github.com/bunsenfeng/kalm
공식 코드https://github.com/bvanaken/explain-BERT-QA
공식 코드How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
https://github.com/bvanaken/visbert
공식 코드https://github.com/bytarnish/agile
공식 코드AGILE: A Novel Reinforcement Learning Framework of LLM Agents
https://github.com/bytedance/MTVQA
공식 코드MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
https://github.com/bytedance/Shot2Story
공식 코드Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
https://github.com/bytedance/lvlm_interpretation
공식 코드Where do Large Vision-Language Models Look at when Answering Questions?
https://github.com/bytedance/salmonn
공식 코드SALMONN: Towards Generic Hearing Abilities for Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.