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
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System
VOGUE: Answer Verbalization through Multi-Task Learning
Papers
GANDR: 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 GenerationTAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding
Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To address this challenge, we propose TAU-Agent, an agentic retrieval-augmen…
Answer GenerationVideo Captioning