Answer Generation
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Benchmarks
WeiboPolls
CICERO
Most implemented
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
RIRAG: Regulatory Information Retrieval and Answer Generation
Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System
VOGUE: Answer Verbalization through Multi-Task Learning
Papers
Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents
Reliable confidence estimation is increasingly central to the trustworthy deployment of language models: a calibrated estimate of the probability that an output is correct decides what to ship, what to escalate, and what…
Answer GenerationGANDR: Claim Auditing for Verifiable Legal Answer Generation
In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the source the system cites. Current grounded-generation pipelines score the…
Answer GenerationBIT.UA at BioASQ 14B: Modular Retrieval with pg_textsearch and Qdrant, and Agent-Based Answer Generation
This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of the BioASQ Task B challenge on biomedical question answering. Building on our previous submissions, we introd…
Question AnsweringAnswer GenerationA Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering
Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence-intensive QA, where answering a question requires synthesizing info…
Answer GenerationFrom Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning
Financial question answering (QA) has emerged as a key benchmark for evaluating the performance of Large Language Models (LLMs) on domain-specific tasks involving complex data formats such as tables, charts, and rich tex…
Synthetic Data GenerationSemantic SimilarityQuestion AnsweringAnswer GenerationLivingRAG: Augmenting Graph RAG with Experience
Graph-based RAG improves multi-hop question answering by organizing evidence as a knowledge graph. However, most existing RAG systems process each query in isolation and discard useful reasoning from the LLM's response a…
Multi-hop Question AnsweringAnswer Generation