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 3701–3750 of 10817 papers

TitleStatusHype
Unleashing the Potentials of Likelihood Composition for Multi-modal Language ModelsCode0
Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents—0
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis—0
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured DataCode0
Semantic Parsing with Candidate Expressions for Knowledge Base Question AnsweringCode0
Addition is All You Need for Energy-efficient Language Models—0
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks—0
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and FilteringCode0
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language ModelsCode0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationCode0
Towards Robust Extractive Question Answering Models: Rethinking the Training MethodologyCode0
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation SystemsCode0
Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding—0
Zero-Shot Multi-Hop Question Answering via Monte-Carlo Tree Search with Large Language Models—0
3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models—0
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations—0
TrojVLM: Backdoor Attack Against Vision Language Models—0
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations—0
Exploring Language Model Generalization in Low-Resource Extractive QACode0
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow—0
Enhancing Explainability in Multimodal Large Language Models Using Ontological Context—0
Rehearsing Answers to Probable Questions with Perspective-Taking—0
Revisiting the Superficial Alignment Hypothesis—0
T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task—0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue—0
Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience—0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
DisGeM: Distractor Generation for Multiple Choice Questions with Span MaskingCode0
Robotic Environmental State Recognition with Pre-Trained Vision-Language Models and Black-Box Optimization—0
DARE: Diverse Visual Question Answering with Robustness Evaluation—0
Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation—0
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering—0
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition—0
Detecting Temporal Ambiguity in QuestionsCode0
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQACode0
Konstruktor: A Strong Baseline for Simple Knowledge Graph Question AnsweringCode0
Unlocking Markets: A Multilingual Benchmark to Cross-Market Question AnsweringCode0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing—0
Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation—0
60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering—0
A Zero-Shot Open-Vocabulary Pipeline for Dialogue UnderstandingCode0
A Unified Hallucination Mitigation Framework for Large Vision-Language ModelsCode0
Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation—0
AsthmaBot: Multi-modal, Multi-Lingual Retrieval Augmented Generation For Asthma Patient Support—0
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language ModelsCode0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?—0
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP—0
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation—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