Generative Question Answering
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Benchmarks
Most implemented
Unified Language Model Pre-training for Natural Language Understanding and Generation
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
CoQA: A Conversational Question Answering Challenge
General-Purpose Question-Answering with Macaw
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
ANAH: Analytical Annotation of Hallucinations in Large Language Models
Papers
BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali
Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LLMs) for Bengali. We introduce BenHalluEval, a fine-grained hallucina…
Generative Question AnsweringGenerative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation
With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as healthcare and biomedicine. However, the c…
Generative Question AnsweringAraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
Recently, extensive research on the hallucination of the large language models (LLMs) has mainly focused on the English language. Despite the growing number of multilingual and Arabic-specific LLMs, evaluating LLMs' hall…
Generative Question AnsweringBeyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs
Previous approaches to persona simulation large language models (LLMs) have typically relied on learning basic biographical information, or using limited role-play dialogue datasets to capture a character's responses. Ho…
Generative Question AnsweringMultiple-choiceQuestion AnsweringStyle TransferEvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering
When addressing professional questions in the biomedical domain, humans typically acquire multiple pieces of information as evidence and engage in multifaceted evidence analysis to provide high-quality answers. Current L…
Answer GenerationGenerative Question AnsweringLanguage ModelingLanguage Modelling+2Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering
To address the hallucination in generative question answering (GQA) where the answer can not be derived from the document, we propose a novel evidence-enhanced triplet generation framework, EATQA, encouraging the model t…
Generative Question AnsweringHallucinationQuestion AnsweringTriplet