https://github.com/nikbpetrov/llms-simulate-humans
공식 코드Limited Ability of LLMs to Simulate Human Psychological Behaviours: a Psychometric Analysis
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/nikhilsab/h-star
공식 코드H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables
https://github.com/nikolamilosevic86/verif.ai
공식 코드https://github.com/nil-zhuang/efficientrag-official
공식 코드EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
https://github.com/nilesh2797/dexml
공식 코드https://github.com/ningmiao/selfcheck
공식 코드SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
https://github.com/niuchuangnn/m3fm
공식 코드Specialty-Oriented Generalist Medical AI for Chest CT Screening
https://github.com/nju-websoft/TSQA
공식 코드https://github.com/nju-websoft/epr-kgqa
공식 코드Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval
https://github.com/nju-websoft/formulaqa
공식 코드FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning
https://github.com/nju-websoft/formulareasoning
공식 코드FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning
https://github.com/nju-websoft/genmc
공식 코드Clues Before Answers: Generation-Enhanced Multiple-Choice QA
https://github.com/nju-websoft/jeeves-gkmc
공식 코드When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
https://github.com/nju-websoft/mmrag
공식 코드https://github.com/nju-websoft/mukg
공식 코드$μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications
https://github.com/nkandpa2/long_tail_knowledge
공식 코드https://github.com/nl4opt/ORQA
공식 코드Evaluating LLM Reasoning in the Operations Research Domain with ORQA
https://github.com/nlp-anonymous-happy/anonymous-kg-guided-nlp
공식 코드https://github.com/nlp-anonymous-researcher/legal-uqa
공식 코드LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering
https://github.com/nlp-tlp/cleangraph
공식 코드CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion
https://github.com/nlp-waseda/traveling-across-languages
공식 코드Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs
https://github.com/nlpcode/tubench
공식 코드TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
https://github.com/nlpdata/evidence
공식 코드Evidence Sentence Extraction for Machine Reading Comprehension
https://github.com/nlpdata/external
공식 코드https://github.com/nlpdata/strategy
공식 코드Improving Machine Reading Comprehension with General Reading Strategies
https://github.com/nnaik39/commvqa
공식 코드CommVQA: Situating Visual Question Answering in Communicative Contexts
https://github.com/nnaik39/context-vqa
공식 코드Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering
https://github.com/nnnoidea/stateful-KGQA
공식 코드https://github.com/noagarcia/ArtVQA
공식 코드A Dataset and Baselines for Visual Question Answering on Art
https://github.com/noagarcia/ROLL-VideoQA
공식 코드Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions
https://github.com/noamrazin/imp_reg_dl_not_norms
공식 코드Implicit Regularization in Deep Learning May Not Be Explainable by Norms
https://github.com/noewangjy/sp-cot
공식 코드Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning
https://github.com/nomothings/charge
공식 코드Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
https://github.com/nooralahzadeh/clg-vqa
공식 코드Improving the Cross-Lingual Generalisation in Visual Question Answering
https://github.com/norakassner/BERT-kNN
공식 코드BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA
https://github.com/norakassner/LAMA_primed_negated
공식 코드Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.