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Tigran T. Tchrakian

2 accepted papers

2025

FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models

EMNLP 2025

Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs thus limiting their reliability in real-world applications where correctness is critical. In this paper, we present FactReasoner, a novel neu

2024

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia

NeurIPS 2024poster

Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated information. However, it remains unclear how LLMs handle knowledge conflicts arising from different augmented retrieved passages,…

Cited by 6SourcePDFScholar