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.