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 3601–3650 of 10817 papers

TitleStatusHype
VideoCon: Robust Video-Language Alignment via Contrast CaptionsCode1
On the Calibration of Multilingual Question Answering LLMs—0
Transformers in the Service of Description Logic-based ContextsCode0
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects—0
German FinBERT: A German Pre-trained Language Model—0
Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional TrainingCode2
Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation—0
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question PromptsCode0
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMsCode1
Towards Verifiable Text Generation with Symbolic References—0
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response GenerationCode0
Attribute Diversity Determines the Systematicity Gap in VQACode0
Temporal Knowledge Question Answering via Abstract Reasoning InductionCode1
ContraDoc: Understanding Self-Contradictions in Documents with Large Language ModelsCode1
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context LearningCode0
Asking More Informative Questions for Grounded Retrieval—0
Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language ModelsCode1
Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset—0
Insights into Classifying and Mitigating LLMs' Hallucinations—0
The ART of LLM Refinement: Ask, Refine, and Trust—0
CPopQA: Ranking Cultural Concept Popularity by LLMs—0
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning—0
Learning to Filter Context for Retrieval-Augmented GenerationCode2
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQACode1
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language QuestionsCode0
TempTabQA: Temporal Question Answering for Semi-Structured Tables—0
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge—0
How good are Large Language Models on African Languages?—0
What Large Language Models Bring to Text-rich VQA?—0
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question AnsweringCode1
A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language ModelsCode1
Hallucination Augmented Recitations for Language Models—0
A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases—0
InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models—0
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language ModelsCode4
Large Language Models are In-context Teachers for Knowledge Reasoning—0
On the Robustness of Question Rewriting Systems to Questions of Varying HardnessCode0
InfMLLM: A Unified Framework for Visual-Language TasksCode1
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs—0
Visual Commonsense based Heterogeneous Graph Contrastive Learning—0
BizBench: A Quantitative Reasoning Benchmark for Business and Finance—0
Knowledgeable Preference Alignment for LLMs in Domain-specific Question AnsweringCode1
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal ModelsCode3
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human PreferencesCode1
Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources—0
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers—0
Agent Lumos: Unified and Modular Training for Open-Source Language AgentsCode2
TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMsCode1
NLQxform: A Language Model-based Question to SPARQL TransformerCode0
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEsCode1
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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