SOTAVerified

Question Answering

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 )

Papers

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Showing 3976–4000 of 10817 papers

TitleStatusHype
Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering—0
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?—0
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning—0
Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms—0
Textbooks Are All You Need II: phi-1.5 technical report—0
NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment—0
Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links—0
Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities—0
AGent: A Novel Pipeline for Automatically Creating Unanswerable QuestionsCode0
MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images—0
Code-Style In-Context Learning for Knowledge-Based Question AnsweringCode1
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in ChineseCode4
LLMCad: Fast and Scalable On-device Large Language Model Inference—0
Introducing "Forecast Utterance" for Conversational Data Science—0
Interpretable Visual Question Answering via Reasoning Supervision—0
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models—0
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison CorpusCode0
Aligning Large Language Models for Clinical TasksCode0
GRASS: Unified Generation Model for Speech-to-Semantic Tasks—0
Exploiting Language Models as a Source of Knowledge for Cognitive Agents—0
ATM: Action Temporality Modeling for Video Question Answering—0
Physically Grounded Vision-Language Models for Robotic Manipulation—0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified