ShortGPT: Efficient Neural Model Compression

• ShortGPT is a family of techniques for compressing Transformer models by eliminating redundant layers, tokens, and weights while preserving essential functionality.
• It employs methods such as Block Influence-guided pruning, iterative distillation, and Kronecker decomposition to achieve significant reductions in size and latency.
• The approach is applied in dialogue systems and physics-informed models, offering practical savings in cost and computational resources with minimal performance loss.

The ShortGPT approach encompasses a family of techniques and algorithms for reducing the computational, memory, and token-footprint costs of large neural models—especially Transformer-based architectures—via explicit compression at the level of model weights, architecture, or input/output representation. The central unifying principle is to identify and safely eliminate redundancy, whether in the structure of multilayer networks, in conversational input sequences, or in generated structured data, all while preserving essential functional capacity and downstream quality. ShortGPT methods span direct layer removal via intrinsic metrics (e.g. Block Influence), context-aware iterative distillation and pruning, grammatically grounded notation minimization for structured data, Kronecker-factor decomposition, and meta-modeling strategies in the physics-informed PDE domain. These approaches achieve substantial reductions in model size, token count, latency, and cost, with empirical tolerances for accuracy degradation rigorously established through ablation studies, human annotations, or statistical benchmarks.

1. Motivations for Compression and Input Reduction

The impetus for ShortGPT-style techniques arises from several practical bottlenecks:

• LLMs and generative architectures—routinely containing billions of parameters—incur prohibitive inference costs, memory footprints, and API charges.
• Syntactic verbosity in data formats (e.g., JSON, YAML) leads to inflated prompt and generation lengths for LLMs, directly impacting token-based billing and latency.
• Redundancy in both neural network layers and multi-turn conversational history is prevalent; not all components contribute significantly to performance or semantic continuity.
• Deployment on resource-constrained hardware, edge devices, or in latency-sensitive applications necessitates compact and efficient model instantiation.

Empirical analysis has shown that up to ∼72% input utterance length reduction in dialog systems yields only a 0.01–0.02 drop in automatic response similarity metrics (ROUGE-L, METEOR, BERTScore), often imperceptible in human judgment (Tao et al., 31 Jan 2024). Similarly, for LLMs, removal of up to one-third of Transformer layers results in sub-10% aggregate score loss across a suite of representative tasks (Kovalev et al., 7 Nov 2025).

2. Architectures and Algorithms for Layer and Weight Compression

Several ShortGPT-inspired algorithms target model weights and architecture:

Block Influence–Guided Layer Removal

ShortGPT introduces the Block Influence (BI) metric to quantify layer redundancy: $$BI_{i} = 1 - \mathbb{E}_{X,t} \left[ \frac{X_{i,t}^\top X_{i+1,t}}{\|X_{i,t}\|_2 \|X_{i+1,t}\|_2} \right]$$ where $X_{i,t}$ denotes the hidden representation at layer $i$ for token $t$. Layers with lowest $BI_i$ (i.e. minimal hidden-state transformation) are deleted, typically reducing depth by 25% with negligible loss in multiple-choice accuracy (Men et al., 6 Mar 2024).

Iterative Layer-wise Distillation

This variant employs a leave-one-out importance estimate:

• Evaluate quality degradation $\Delta_i = S(M^n) - S(M^n \setminus \{i\})$ on a diverse dataset suite per layer.
• Remove the lowest-importance layer, then fine-tune the pruned model using a joint loss combining output-distribution KL divergence and hidden-state mean-squared error:

$$\mathcal{L}(\theta) = \alpha\, D_{KL}(p_T \,||\, p_S) + \beta \|h_T - h_S\|_2^2$$

Optimal tradeoff observed at $\alpha=1/100, \beta=1$ (Kovalev et al., 7 Nov 2025). This protocol preserves performance more effectively than static BI-based pruning, particularly protecting the functional contributions of the outermost layers.

Kronecker Decomposition of Linear Layers

KnGPT2 employs rank-1 Kronecker product approximations for key weight matrices: $$W \approx A \otimes B, \quad (A,B) = \arg\min_{(A',B')} \|W - A' \otimes B'\|_F^2$$ Initialization uses leading singular vectors; performance is restored via very light (1-epoch) intermediate-layer knowledge distillation (Edalati et al., 2021).

Compressed Decoder-Only Architectures

ShortGPT variants include:

• ParallelGPT: splits intermediate representations between two half-depth branches, recombined by a learned weight. Enables parallel training and 50% inference cost reduction in one-branch mode.
• LinearlyCompressedGPT: halves hidden dimensions after every two blocks, reducing parameter count and FLOPs by up to 36%.
• ConvCompressedGPT: uses 1D convolutional downsampling instead of linear layers, introducing additional inductive bias and further FLOP/latency savings. Loss profiles and generation quality are virtually unchanged compared to full-size reference models for next-token prediction (Suresh et al., 22 Apr 2024).

3. Pruning and Input Compression in Conversation Modeling

ShortGPT methodologies extend to dialogue systems:

• Multi-turn conversations are represented as $C = \{U_1, U_2, U_3\}$ (question, answer, follow-up). The answer $U_2$ is compressed by prompting a LLM with length constraints, using empirical thresholds ($t_{\text{long}} = 7$, $t_{\text{short}} = 4$ words).
• Up to 72% reduction in $U_2$ length yields negligible difference in $U_3$ quality, as measured by overlapping distribution in both automatic (ROUGE-L, METEOR, BERTScore) and human ratings (inter-annotator $\kappa = 0.58$) (Tao et al., 31 Jan 2024).
• Human raters identify original and compressed responses as "equally good" or indistinguishable in the majority of cases.

Guidelines include preserving context-setting questions, calibrating length thresholds to domain, and spot-checking compressed outputs for meaning retention.

4. Token-Efficient Generation for Structured Data

ShortGPT generalizes to token minimization for structured data (e.g. JSON for visualization):

• Domain-Specific Shorthand (DSS) is formally established as a context-free grammar (CFG), $G = (N, \Sigma, P, S)$, generating bijective encodings between standard and compressed forms. The mapping $\phi$ satisfies lossless, linear-time conversions.
• Token counts are theoretically and empirically reduced by a factor $R = L_v / L_d \approx 3$–$5$.
• Parsers for DSS are linear-time (LL(1) via recursive descent). Quantitative results show mean $R = 4.1$ with statistically significant reduction in token budget, cost (by $\geq$75%), and latency (70%).
• The approach is directly applicable to any structured domain with regular schemas and can be automated for additional formats (Kanyuka et al., 14 Jun 2024).

Practical use requires grammar authoring and inclusion of DSS grammar/exemplars in the LLM prompt, with deterministic inference via temperature $=0$.

5. Sparse Meta-Networks in Physics-Informed PDE Solvers

The ShortGPT philosophy is instantiated in S$^2$GPT-PINN:

• The architecture is a network of pre-trained Physics-Informed Neural Networks (PINNs) as nonparametric activation functions, with a single trainable linear layer combining $n = 10$–$25$ such PINNs.

$$\Psi_{\mathrm{S}^2\mathrm{GPT}}(x, t; \mu) = \sum_{i=1}^n c_i(\mu) \Psi_{\mathrm{NN}^{\mu^i}}(x, t)$$

• Parameter selection proceeds via a rigorous greedy algorithm maximizing an error indicator over the training parameter set.
• Hyper-reduced collocation: physics-informed loss is computed on $M = 2n - 1$ points, orders of magnitude below standard PINNs.
• This setup achieves $10^{-6}$–$10^{-8}$ accuracy with 100–1000$\times$ lower online time and similar reductions in parameter count and collocation size compared to standard PINNs (Ji et al., 25 May 2025).

Limitations include the need for domain-specific pretraining, orthonormalization of the basis PINNs, and adaptation to higher-dimensional parameter spaces.

6. Practical Implementation Considerations and Trade-Offs

• All approaches achieve best cost-quality trade-off when compression or pruning is guided by actual performance degradation (e.g., via held-out set scoring or BI measures) rather than purely internal metrics.
• For layer removal, structural pruning is more robust than width-wise pruning; however, selective preservation of high-importance blocks (position-encoding, early linguistic) is advised for generative or step-by-step tasks (Men et al., 6 Mar 2024).
• ShortGPT techniques are generally orthogonal to quantization and can be combined for compound gains; e.g., layer-pruned plus 4-bit quantized LLaMA2-7B maintains nearly full MMLU accuracy (Men et al., 6 Mar 2024).
• Proposed methods may fall short for highly generative tasks in code/math, which exhibit higher sensitivity to removal of computation paths; post-pruning fine-tuning or knowledge distillation may be required to recover performance.

A plausible implication is that the ShortGPT paradigm—when rigorously validated via end-to-end downstream evaluation—enables scalable, deployable LLM architectures and efficient data pipelines without material compromise in quality for a wide range of tasks.