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Open-Domain Question Answering

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하위 태스크: 0
평가표: 15
보존된 평가 기록: 109

Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/lockon-n/self-prompting

공식 코드

Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

https://github.com/lumia-group/fouriertransformer

공식 코드

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

https://github.com/lupantech/ScienceQA

공식 코드

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

https://github.com/martiansideofthemoon/hurdles-longform-qa

공식 코드

Hurdles to Progress in Long-form Question Answering

https://github.com/matejulcar/slot5-tools

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Sequence to sequence pretraining for a less-resourced Slovenian language

https://github.com/mayer123/udt-qa

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Open Domain Question Answering with A Unified Knowledge Interface

https://github.com/microsoft/seed-encoder

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Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder

https://github.com/microsoft/simxns

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Allies: Prompting Large Language Model with Beam Search

https://github.com/microsoft/taser

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Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering

https://github.com/mingdali6717/ensemble-of-retrievers

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Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models

https://github.com/morningmoni/GAR

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Generation-Augmented Retrieval for Open-domain Question Answering

https://github.com/mrjleo/ranking-models

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Exploiting Sentence-Level Representations for Passage Ranking

https://github.com/mukhal/lepus

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Few-shot Reranking for Multi-hop QA via Language Model Prompting

https://github.com/mukhal/promptrank

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Few-shot Reranking for Multi-hop QA via Language Model Prompting

https://github.com/narabzad/llm-relevance-judgement-comparison

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Benchmarking LLM-based Relevance Judgment Methods

https://github.com/nijianmo/arc-etrr-code

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Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

https://github.com/nilesh2797/dexml

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Dual-Encoders for Extreme Multi-Label Classification

https://github.com/nlpdata/evidence

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Evidence Sentence Extraction for Machine Reading Comprehension

https://github.com/noewangjy/sp-cot

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Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning

https://github.com/norakassner/BERT-kNN

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BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA

https://github.com/noviscl/answerequiv

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What's in a Name? Answer Equivalence For Open-Domain Question Answering

https://github.com/noviscl/calibrateqa

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Re-Examining Calibration: The Case of Question Answering

https://github.com/nusnlp/amanda

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A Question-Focused Multi-Factor Attention Network for Question Answering

https://github.com/nyu-dl/SearchQA

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SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

https://github.com/openai/sparse_attention

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Generating Long Sequences with Sparse Transformers

https://github.com/openmatch/univl-dr

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Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval

https://github.com/openmoss/say-i-dont-know

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Can AI Assistants Know What They Don't Know?

https://github.com/orionw/disinformation-defense

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Defending Against Disinformation Attacks in Open-Domain Question Answering

https://github.com/oriram/spider

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Learning to Retrieve Passages without Supervision

https://github.com/oriyor/ret-robust

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Making Retrieval-Augmented Language Models Robust to Irrelevant Context

https://github.com/paddlepaddle/rocketqa

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RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/pkasela/desire-me

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DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts

https://github.com/princeton-nlp/DensePhrases

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Learning Dense Representations of Phrases at Scale

https://github.com/princeton-nlp/entityquestions

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Simple Entity-Centric Questions Challenge Dense Retrievers

https://github.com/psunlpgroup/veras

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VerAs: Verify then Assess STEM Lab Reports

https://github.com/rajarshd/Multi-Step-Reasoning

공식 코드

Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering

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