Applying pruning techniques, whether removing individual weights or entire structural components, inevitably alters the learned representations and computational pathways within an LLM. While the goal is to achieve this with minimal performance loss, a comprehensive analysis is necessary to understand the full spectrum of effects on the model's capabilities. Simply measuring perplexity or aggregate scores on a benchmark suite often provides an incomplete picture, especially for generative models or models deployed for specific downstream applications.

A rigorous evaluation framework for pruned LLMs involves assessing multiple dimensions of performance, going beyond standard accuracy metrics.

Assessing Core Language Modeling Capabilities

The most immediate impact of pruning is often observed on intrinsic language modeling metrics.

Perplexity: This remains a fundamental measure. Track perplexity on a held-out dataset as sparsity increases. Expect a non-linear relationship: initial pruning might have minimal impact, but degradation often accelerates past a certain sparsity threshold. The shape of this curve depends heavily on the pruning method, the fine-tuning strategy post-pruning, and the model architecture.
Standard Benchmarks (GLUE, SuperGLUE): Evaluate performance on diverse task suites like GLUE or SuperGLUE. Analyze performance degradation per task. Some tasks might be more sensitive to pruning than others, potentially indicating which capabilities (e.g., reasoning, sentiment analysis) are more reliant on the pruned parameters. For instance, tasks requiring fine-grained semantic understanding might suffer more than simpler classification tasks.

Relationship between increasing model sparsity and the resulting increase in perplexity for different pruning techniques. Note the potentially steeper degradation at higher sparsity levels.

Evaluating Generative Performance

For generative LLMs, standard benchmarks are insufficient. Pruning can subtly affect the quality, coherence, and diversity of generated text.

Fluency and Coherence: Assess generated text for grammatical correctness, logical flow, and consistency. Automated metrics like BLEU or ROUGE (typically used for translation/summarization) can provide some signal but often correlate poorly with human judgment of overall quality. Qualitative analysis by humans is frequently required.
Repetition and Diversity: Pruned models might exhibit increased repetition or reduced lexical diversity. Analyze n-gram repetition rates and metrics like Distinct-1/Distinct-2 (measuring the ratio of unique unigrams/bigrams).
Factuality and Hallucination: Investigate whether pruning increases the model's tendency to generate factually incorrect statements (hallucinations). This requires specialized evaluation sets or careful manual review.
Instruction Following and Creativity: For instruction-tuned models, assess their ability to follow complex instructions accurately after pruning. Evaluate subjective qualities like creativity in tasks such as story generation or brainstorming.

Impact on Downstream Tasks and Knowledge Retention

Pruning can disproportionately affect knowledge encoded during pre-training or specific skills acquired during fine-tuning.

Task-Specific Performance: Measure performance degradation on the specific downstream tasks the model is intended for (e.g., summarization quality using ROUGE, translation quality using BLEU/COMET, code generation accuracy using pass@k metrics).
Knowledge Probing: Use targeted probes or question-answering datasets (e.g., TriviaQA, Natural Questions) to assess if specific factual knowledge has been lost. Analyze if pruning affects certain knowledge domains more than others.
Catastrophic Forgetting: Evaluate if the pruning process, especially if combined with extensive fine-tuning, leads to forgetting of general capabilities learned during pre-training. This involves testing on a broad range of tasks, not just the fine-tuning target. Iterative pruning schedules with intermediate fine-tuning often aim to mitigate this.

Analyzing Architectural Sensitivity

Different components within the transformer architecture exhibit varying sensitivity to pruning.

Attention Heads vs. FFN Layers: Structured pruning often targets specific components. Analyze the impact of pruning attention heads versus removing parameters from feed-forward network (FFN) layers. Pruning certain heads might affect long-range dependency modeling, while FFN pruning might impact factual recall or specific learned transformations. Research suggests FFN layers often contain more redundant parameters compared to attention mechanisms.
Layer Sensitivity: Parameters in different layers might have varying importance. Early layers might capture more general features, while later layers handle more abstract representations. Pruning strategies sometimes apply different sparsity levels to different layers based on sensitivity analyses.

Interaction Between Pruning Type and Impact

The choice between unstructured and structured pruning significantly influences the observed effects.

Unstructured Pruning: Often achieves higher sparsity levels before significant performance drops, but the resulting irregular sparsity pattern can be difficult to accelerate on standard hardware without specialized kernels or compiler support. The impact might be diffuse across model capabilities.
Structured Pruning: Directly removes computationally significant blocks (channels, heads, layers). This provides more predictable latency improvements on hardware but might lead to sharper performance drops if critical structures are removed. The impact is often more localized to the functions performed by the pruned blocks (e.g., removing specific attention heads might impair specific relational reasoning).

Fairness, Robustness, and Bias Considerations

While Chapter 7 provides a deeper look, it's important to initially assess if pruning introduces or exacerbates issues related to fairness or robustness. Does the pruned model exhibit increased bias towards certain demographics? Is it more susceptible to adversarial attacks or out-of-distribution inputs? Optimization techniques can sometimes unintentionally remove representations important for minority groups or robustness checks. Preliminary checks using fairness benchmarks (e.g., BOLD, ToxiGen) or robustness tests are advisable.

In summary, evaluating the effects of pruning requires a multi-faceted approach. Combine automated metrics, performance on specific downstream tasks, qualitative assessments of generation, and analyses of architectural component sensitivity. This comprehensive evaluation ensures that the efficiency gains from pruning do not come at an unacceptable cost to the model's essential capabilities, aligning the optimized model with its intended deployment requirements.