AI Skill Report Card
Analyzing LLM4Rec Papers
YAML--- name: analyzing-llm4rec-papers description: Analyzes LLM-based recommendation system research papers to extract architecture details, deployment considerations, key innovations, and comparative analysis. Use when reviewing academic papers on LLM4Rec systems or comparing recommendation models. ---
Analyzing LLM4Rec Papers
Quick Start10 / 15
Python# Paper analysis template paper_analysis = { "architecture": "decoder-only/encoder-decoder", "training_phase": "pre-train/post-train", "parameters": "estimated count", "deployment_complexity": "low/medium/high", "key_innovation": "main contribution", "comparison": {"HSTU": "differences", "TIGER": "differences"} }
Recommendation▾
Replace abstract Quick Start dictionary with actual paper analysis workflow - show step-by-step analysis of a real paper excerpt
Workflow12 / 15
Progress:
- Identify model architecture type
- Determine training methodology
- Estimate parameters and deployment difficulty
- Extract key technical innovations
- Analyze network layers and dimensions
- Compare with HSTU and TIGER baselines
Step 1: Architecture Classification
- Decoder-only: Autoregressive generation (GPT-style)
- Encoder-decoder: Bidirectional encoding + generation (T5-style)
- Training phase: Pre-training from scratch vs. fine-tuning existing LLMs
Step 2: Parameter Estimation
Look for:
- Model size mentions (e.g., "7B parameters", "base/large variant")
- Layer counts and hidden dimensions
- Embedding dimensions
- Attention heads
Calculate: parameters ≈ layers × hidden_dim² × 12 (rough estimate)
Step 3: Deployment Analysis
Low complexity: <1B parameters, efficient attention
Medium complexity: 1-10B parameters, standard transformers
High complexity: >10B parameters, requires distributed inference
Step 4: Key Innovation Extraction
Focus on:
- Structure: Novel attention patterns, layer modifications
- Activation functions: GELU, SwiGLU, custom activations
- Loss functions: Contrastive, ranking, generation losses
- Training objectives: Masked language modeling, next-token prediction
Step 5: Layer Analysis
For each key component:
- Input/output dimensions
- Parameter count per layer
- Computational complexity
- Memory requirements
Step 6: Baseline Comparison
Compare against:
- HSTU: Hierarchical Sequential Transduction Units
- TIGER: Temporal Interest and Global Enhancement for Recommendation
Recommendation▾
Make examples more concrete with actual paper titles, specific architectures, and numerical results rather than generic templates
Examples12 / 20
Example 1: Input: Paper describing RecLLM with 6-layer decoder, 768 hidden dim, 12 attention heads Output:
- Architecture: Decoder-only, post-train fine-tuning
- Parameters: ~85M (6 × 768² × 12 ≈ 42M + embeddings)
- Deployment: Low complexity
- Key innovation: User-item sequential encoding with contrastive loss
Example 2: Input: LLM4Rec paper with T5-base backbone (220M params) Output:
- Architecture: Encoder-decoder, post-train adaptation
- Parameters: 220M base + 50M adaptation layers ≈ 270M
- Deployment: Medium complexity
- Innovation: Cross-attention between user history and item features
Recommendation▾
Remove over-explanations like parameter calculation formulas and basic ML concepts that Claude already understands
Best Practices
- Parameter estimation: Use layer counts × hidden dimensions as primary indicator
- Architecture identification: Look for generation vs. understanding tasks
- Innovation assessment: Focus on domain-specific adaptations to base LLM
- Dimension tracking: Pay attention to sequence lengths and embedding sizes
- Comparison fairness: Ensure similar experimental setups when comparing models
Common Pitfalls
- Don't confuse backbone model size with total trainable parameters
- Don't overlook adapter/LoRA parameters in fine-tuned models
- Don't assume decoder-only means pre-training from scratch
- Don't ignore computational complexity beyond parameter count
- Don't compare models without considering dataset and evaluation differences