Large Audio-Language Models have made significant advances in audio-text instruction following and multimodal interaction. However, due to their text-conditioned generation architecture, current LALMs tend to be overly influenced by input text prompts. When acoustic evidence conflicts with textual prompts, incorrect or irrelevant textual cues may steer model predictions away from the audio. In this work, we systematically analyze this challenge and show that representative LALMs exhibit a persistent audio-text imbalance during decoding, where attention to audio tokens is weakened across generation steps. To address the imbalance, we propose Modality-Specific Gating (MSG), an acoustic calibration mechanism that improves audio grounding with a small number of additional parameters. MSG introduces a token-wise, query-conditioned bias to the attention logits of audio tokens before softmax, while leaving the original attention computation unchanged. With parameter-efficient fine-tuning, MSG enhances acoustic evidence utilization, leading to consistently improved performance on vocal sound classification and speech emotion recognition, as well as strong robustness under adversarial or irrelevant text prompts.

Joint-encoding zero-shot relation extraction classifies all candidate relation labels in a single Transformer forward pass, achieving strong accuracy and high throughput. However, under a controlled full-vocabulary diagnostic, two distributional pathologies surface: unnormalized dot-product scoring causes recall collapse, with certain types receiving zero predictions, while the per-instance BCE loss leaves gold and negative scores entangled beyond any reliable threshold—confidence ambiguity. We propose SCAR, which keeps the baseline pathway intact and stabilizes both distributions via two auxiliary branches: a gradient-isolated coarse-to-fine cascade with Z-score normalization that restores cross-relation comparability, and a training-only contrastive alignment module that separates gold from negative scores. On Wiki-ZSL (m=15), SCAR raises Macro F1 from 75.9% to 83.0% and cuts zero-recall types from 35 to 1, suggesting that small-pool ZSRE protocols can obscure failure modes critical to large-vocabulary deployment.