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cs.CL2026
Factuality on Demand: Controlling the Factuality-Informativeness Trade-off in Text Generation
Ziwei Gong, Yanda Chen, Julia Hirschberg +4
Large language models (LLMs) encode knowledge with varying degrees of confidence. When responding to queries, models face an inherent trade-off: they can generate responses that ar…
cs.CL2026
Business Logic-Driven Text-to-SQL Data Synthesis for Business Intelligence
Jinhui Liu, Ximeng Zhang, Yanbo Ai +1
Evaluating Text-to-SQL agents in private business intelligence (BI) settings is challenging due to the scarcity of realistic, domain-specific data. While synthetic evaluation data…
cs.CL2024★ 1 cited
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…