arXiv Computation and Language
Aug 31

A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls

The paper introduces DualEvasion, a benchmark that evaluates evasion detection in earnings call Q&A using both textual transcripts and vocal cues. It contains 505 annotated question‑answer pairs from 60 calls, each labeled for textual evasion (direct vs. evasive) and speaker confidence (confident vs. unconfident). Experiments show that current multimodal models struggle to detect vocal confidence, especially in unconfident responses, and that providing speaker‑level references only modestly improves performance, leaving a significant gap compared to humans.

By Mirae Kim, Seonghun Jeong, Youngjun Kwak
arXiv AI
Sep 12

When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting

The paper introduces a synthetic benchmark for multimodal time‑series forecasting that evaluates how well text annotations contribute to predictions. By generating controlled signals with semantically correct, incorrect, and irrelevant annotations, the authors can precisely measure the true information content. Six mutual‑information estimators (KSG, MINE, InfoNCE, CCA, PID, and V‑information) are tested, all correctly ranking useful annotations and enabling annotation auditing without model training. The benchmark also highlights each estimator’s limitations and validates findings on seven real datasets, providing practical guidelines for metric implementation.

By Emma Andrews, Gianmarco Mengaldo