Haocheng Mei, Yuchuan Tian, Ye Yuan, Chao Xu, Allen Chen, Kai Han, Yunhe Wang
Commercial language model APIs return only text, allowing a provider to cut costs by quietly routing a fraction of user requests to a cheaper substitute model while billing for the advertised one. Detecting such partial substitution is challenging because honest providers also alter the output distribution through undocumented decoding and serving choices, such as temperature scaling, sampling truncation, repetition and presence penalties, and default system prompts, forcing an audit to test against a composite null of legitimate configurations rather than a single reference. Our key insight is that these legitimate variations primarily shift response typicality across the entire batch, whereas partial substitution injects individual responses that remain anomalous under the claimed model. Building on this, we introduce API-TAIL, a typicality audit that first absorbs batch-level shifts by aligning a single effective temperature to center standardized surprisal scores across the audited batch, and then flags the endpoint if the number of remaining tail responses exceeds a threshold calibrated across legitimate configurations. Evaluated on real-world API responses and constructed substitution mixtures, API-TAIL controls false alarms across diverse honest providers and detects low-rate partial substitution with a small query budget. Under a common composite calibration, it outperforms the adapted rank- and MMD-based audit baselines.
Haocheng Mei, Yuchuan Tian, Ye Yuan, Enhua Wu, Chao Xu, Allen Chen, Kai Han, Yunhe Wang
JEV decision models pair a language model backbone with a lightweight scoring head to select among natural-language options. Direct selection compares candidate answers within one request, whereas entailment reranking evaluates each candidate in a separate natural language inference (NLI) request over fixed relation options. We examine whether the readout and adaptation choices used for direct selection remain effective for cross-request reranking. We combine a mean-span readout with selective backbone adaptation. Mean-span pooling replaces the option-ending pointer with an average of the contextualized states inside each NLI option, without adding head parameters. Selective adaptation retains low-rank updates in attention and gated DeltaNet projections while removing feed-forward adapters. On MC11, which averages accuracy over eleven reranking configurations from seven sources, selective adaptation improves NeoHorse-1-4B accuracy over the all-module baseline while cutting trainable parameters by more than half, and mean-span pooling yields further consistent gains at the same parameter count.
Xiaoyu Liu, Yun Zhang Hong Kong University of Science and Technology, Wei Li, Simiao Li Huawei Noah's Ark Lab, Xudong Huang Huawei Technologies Ltd., Hanting Chen Peking University, Yehui Tang Huawei Technologies Ltd., Jie Hu Huawei Technologies Ltd. 等 10 位作者
Knowledge distillation is crucial for compressing Large Language Models (LLMs), enabling smaller student models to learn from larger teacher models. However, existing LLM distillation methods overly rely on student-generated outputs, which may introduce generation errors and misguide the distillation process. Moreover, existing distillation loss functions struggle to align the most informative part due to the complex output distributions of LLMs. To address these problems, we propose a multi-granularity semantic revision method for LLM distillation. At the sequence level, we propose a sequence correction and re-generation (SCRG) strategy. SCRG identifies error tokens by calculating the semantic cognitive difference between teacher and student outputs, corrects them using teacher-generated tokens, and re-generates the sequence to minimize errors. At the token level, we design a distribution adaptive clipping Kullback-Leibler (DAC-KL) loss, which uses a learnable sub-network to focus on semantically dense areas of the teacher’s output, reducing the impact of redundant information. At the span level, we utilize span priors to compute probability correlations within sequences, ensuring consistency between teacher and student outputs to enhance semantic information transfer. Extensive experiments on models ranging from 0.1B to 13B parameters demonstrate the effectiveness of our approach compared to existing methods.
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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