3 papers
q-fin.GN2025
The Memorization Problem: Can We Trust LLMs' Economic Forecasts?
Alejandro Lopez-Lira, Yuehua Tang, Mingyin Zhu
Large language models (LLMs) cannot be trusted for economic forecasts during periods covered by their training data. Counterfactual forecasting ability is non-identified when the m…
cs.AI2024
FullStack Bench: Evaluating LLMs as Full Stack Coders
Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng +53
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most exist…
cs.CL2024
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
Xueyu Hu, Ziyu Zhao, Shuang Wei +14
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-e…