Researchers have introduced TopBench, a new benchmark designed to evaluate Large Language Models (LLMs) on their ability to perform implicit prediction and reasoning over tabular data. The benchmark includes 779 samples across four sub-tasks, such as decision making and treatment effect analysis, requiring models to produce both text and structured tables. Experiments indicate that current LLMs often struggle with recognizing the latent intent behind queries, frequently defaulting to simple data lookups instead of performing predictive reasoning. AI
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IMPACT Evaluates LLM capabilities in implicit prediction and reasoning over tabular data, highlighting current limitations in intent recognition.
RANK_REASON New benchmark paper published on arXiv.