On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024 · 237 citations)
Building strong NLP models depends on large amounts of diverse, well-labelled data. Collecting such data from people is expensive, sometimes impossible because of scarcity or privacy, and still prone to bias and error. LLMs can produce fluent text and follow instructions closely, which makes them attractive data…
Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection (2026)
Debt collection is a demanding negotiation setting. Existing negotiation benchmarks treat users as rational agents whose preferences never change, but real debtors vary widely in emotion, reasoning ability and manner of speaking. The authors had access to confidential collector–debtor conversations from a fintech…
FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026)
Financial analysts often need evidence that is spread across several filings, non-adjacent pages, and different formats such as prose, tables, and charts.

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Updated 2 November 2026