aiqbench analysis
This arXiv study finds that gating knowledge distillation, a common way to train smaller language models from larger teachers, adds little for low-resource Bangla generation. Plain distillation did not beat cross-entropy fine-tuning on gold outputs, and most of its measured benefit came from regularisation, which teacher-free label smoothing largely matched. Readers should note the narrow scope (Bangla-centric tasks and small students) and the mostly single-seed experiments, but the result is a useful caution against assuming that more sophisticated teacher-weighting schemes help.
Label Smoothing in Disguise? Gated Knowledge Distillation for Low-Resource Bangla Generation
arXiv:2611.01065v1 Announce Type: cross Abstract: Gated knowledge distillation (KD) weights teacher signals per token or example, aiming to trust an imperfect teacher only where it seems reliable. We test two gates: EWAD (entropy-weighted agreement-aware distillation), which gates tokens by teacher confidence, and CHAD (counterfactual harm-aware distillation), which gates examples by gradient alignment with validation data. Across six Bangla-centric tasks, three student models (60M to about 2B parameters), and two teacher families, no gate significantly improves plain KD in 45 comparisons. KD also fails to beat cross-entropy (CE) fine-tuning on gold outputs in 40 single-seed comparisons or three-seed runs on four tasks; CE leads on every summarisation metric. The main benefit of KD is regularisation: over ten fixed epochs, CE students lose 0.030 to 0.044 token-F1 from their early-stopped checkpoint, versus 0.005 to 0.025 for distilled students. Teacher-free label smoothing closes most of this gap. In one comparison, a teacher 0.29 token-F1 worse yields students at most 0.009 worse. Thus, gating may change regularisation strength but has little teacher knowledge to select. Findings are limited to Bangla-centric tasks and small students. We will release code, data splits, predictions, and scores.
Title:Label Smoothing in Disguise? Gated Knowledge Distillation for Low-Resource Bangla Generation
Abstract:Gated knowledge distillation (KD) weights teacher signals per token or example, aiming to trust an imperfect teacher only where it seems reliable. We test two gates: EWAD (entropy-weighted agreement-aware distillation), which gates tokens by teacher confidence, and CHAD (counterfactual harm-aware distillation), which gates examples by gradient alignment with validation data. Across six Bangla-centric tasks, three student models (60M to about 2B parameters), and two teacher families, no gate significantly improves plain KD in 45 comparisons. KD also fails to beat cross-entropy (CE) fine-tuning on gold outputs in 40 single-seed comparisons or three-seed runs on four tasks; CE leads on every summarisation metric. The main benefit of KD is regularisation: over ten fixed epochs, CE students lose 0.030 to 0.044 token-F1 from their early-stopped checkpoint, versus 0.005 to 0.025 for distilled students. Teacher-free label smoothing closes most of this gap. In one comparison, a teacher 0.29 token-F1 worse yields students at most 0.009 worse. Thus, gating may change regularisation strength but has little teacher knowledge to select. Findings are limited to Bangla-centric tasks and small students. We will release code, data splits, predictions, and scores.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2611.01065 [cs.CL]
(or arXiv:2611.01065v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.01065 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 293 words