Qingrui Li, Haowei Lou, Chengkai Huang, Quan Z. Sheng, Lina Yao
Pretrained audio encoders are increasingly used to provide content representations for music recommendation, but their downstream value can change substantially with the recommendation interface through which they are used. We study this effect across six pretrained audio encoders and three representative settings: content-based retrieval, sequential recommendation, and Semantic-ID-based generative recommendation. Using matched random-feature and ID-only controls, we distinguish absolute recommendation performance from gains over content-free inputs. Across two music datasets and a full-catalog evaluation, music-pretrained and audio-text-aligned encoders are generally stronger when their pretrained geometry is used directly or converted into Semantic IDs, while these differences become much smaller after interaction-based adaptation in SASRec. We further examine how audio representations should be discretized for generative recommendation by varying residual-quantization width, depth, and retained layers. At epoch 10, depth one performs best at every tested width, while larger codebooks generally help at depth three; the best single-layer subset also outperforms the full three-layer identifier in every evaluated comparison. For CLAP-Music, the third-layer identifier ranks higher under both original and shuffled assignments, whereas the full three-layer identifier gains more from the original assignment. Better reconstruction likewise does not ensure better ranking. Overall, our results show that the utility of pretrained audio representations is interface-dependent and that Semantic-ID quality should be assessed by downstream ranking rather than representation fidelity alone.
Rui Wang Adobe Systems, Junda Wu University of California, San Diego, Yu Xia University of California, San Diego, Tong Yu Adobe Research, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra Adobe Systems, Lina Yao University of New South Wales 等 9 位作者
Large Language Models (LLMs) are susceptible to indirect prompt injection attack, where the model inadvertently responds to instructions injected into the prompt context. This vulnerability stems from LLMs’ inability to distinguish between data and instructions within a prompt. We propose CachePrune that defends against this attack by identifying and pruning neurons associated with instruction-following, during KV cache encoding of the prompt context. The pruning steers the LLM toward interpreting the context purely as data rather than as instructions to follow. To identify these neurons, we introduce a neural attribution mechanism guided by a preferential attribution loss, and theoretically connect this loss to an upper bound of the Direct Preference Optimization (DPO) objective. Further, we improve on the fidelity of neural attribution by leveraging an observed triggering effect in instruction-following. Our approach does not interfere with prompt formatting or incur test-time overhead in response generation. Experiments show that CachePrune significantly reduces the attack success rate while preserving the LLM’s ability to follow user instructions.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Chuhan Wang University of Notre Dame, Xintong Li, Jennifer Yuntong Zhang, Junda Wu University of California, San Diego, Chengkai Huang, Lina Yao University of New South Wales, Julian McAuley University of California, San Diego, University of California, San Diego, Jingbo Shang University of California, San Diego
Multimodal large language models often struggle with faithful reasoning in complex visual scenes, where intricate entities and relations require precise visual grounding at each step. This reasoning unfaithfulness frequently manifests as hallucinated entities, mis-grounded relations, skipped steps, and over-specified reasoning. Existing preference-based approaches, typically relying on textual perturbations or answer-conditioned rationales, fail to address this challenge as they allow models to exploit language priors to bypass visual grounding. To address this, we propose SceneAlign, a framework that leverages scene graphs as structured visual information to perform controllable structural interventions. By identifying reasoning-critical nodes and perturbing them through four targeted strategies that mimic typical grounding failures, SceneAlign constructs hard negative rationales that remain linguistically plausible but are grounded in inaccurate visual facts. These contrastive pairs are used in Direct Preference Optimization to steer models toward fine-grained, structure-faithful reasoning. Across seven visual reasoning benchmarks, SceneAlign consistently improves answer accuracy and reasoning faithfulness, highlighting the effectiveness of grounding-aware alignment for multimodal reasoning.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表