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UDHGAM ML Challenge — Prompt Difficulty Regression

Overview

This project tackles a regression problem on tokenized prompts, where each input prompt is represented as:

  • input_ids (integer tokens)
  • attention_mask
  • a continuous target label in [0, 1]

The goal is to predict the label with minimum Mean Absolute Error (MAE).

This repository documents not only the final models, but the entire modeling journey:

  • architecture exploration
  • ensemble design
  • Mixture-of-Experts (MoE)
  • feature engineering
  • leakage detection
  • leaderboard overfitting analysis

Final Result:

  • Top-10 finish on Private Leaderboard
  • Team: ML Monarchs

Dataset

Input

Each sample contains:

  • input_ids: List[int] — tokenized prompt (max length 256)
  • attention_mask: List[int] — valid token positions
  • label: float ∈ [0, 1] (train only)

Key Properties

  • Vocabulary size: ~50k tokens
  • Variable sequence lengths
  • Tokens are obfuscated (no semantic meaning)
  • No external metadata

Implication

Since token semantics are unavailable, the task relies heavily on:

  • structural patterns
  • token repetition
  • entropy
  • sequence dynamics

Validation Strategy

  • 5-fold cross-validation using predefined folds
  • Strict separation of train / validation
  • Extensive checks for:
    • duplicate prompts
    • memorization leakage
    • fold contamination

Only CV-safe scores were trusted during development.

Base Architectures Explored

1. CNN-based Models

  • Multi-kernel CNNs (kernel sizes 3, 5, 7)
  • Mean pooling + max pooling
  • Lightweight and strong baselines

2. GRU-based Models

  • Bidirectional GRUs
  • Mean + max pooling
  • Segment-wise pooling (beginning / middle / end)

3. Attention Variants

  • Attention-pooled CNNs
  • Attention-pooled GRUs
  • Stability fallback using mean pooling

4. Dilated CNNs

  • Dilations: 1, 2, 4, 8
  • Larger receptive fields without deeper stacks

Each model outputs a sigmoid-scaled regression value.

Hand-Engineered Features

In addition to neural embeddings, multiple structural features were extracted:

Core Features

  • Normalized sequence length
  • Unique token ratio
  • Token entropy
  • Adjacent repetition ratio

Advanced Structural Features

  • Constraint score (tokens appearing ≥3 times)
  • Relative position variance (RPC)
  • Boundary density (tokens concentrated at 25/50/75%)
  • Windowed entropy shifts (phase changes)
  • Global token rarity profiles
  • Compression ratio (zlib)

These features captured prompt regularity and rigidity, which showed moderate correlation with labels.

Ensemble Strategy

Level-1: Model Zoo

Eight diverse neural models were trained:

  • CNN
  • CNN (different seed)
  • BiGRU
  • BiGRU (different seed)
  • Segmented GRU
  • Attention GRU
  • Attention CNN
  • Dilated CNN

Out-of-fold (OOF) predictions were collected for all models.

Level-2: Meta Models

Multiple stacking approaches were tested:

  • LightGBM regression
  • Logit-space regression
  • Disagreement features (mean, std, range)
  • Per-model deviation features
  • Defensive regularization variants
  • Ridge and ElasticNet baselines

Best CV MAE achieved: ~0.146
Best Private LB MAE: ~0.150

Mixture of Experts (MoE)

To address heterogeneous prompt behavior, several MoE strategies were explored.

Hard-Binned MoE

Experts trained on bins defined by:

  • sequence length
  • entropy
  • constraint score

Predictions were averaged across experts.

Gated MoE

  • Experts trained per bin
  • Separate gate model predicts expert weights
  • Softmax-normalized expert routing

MoE yielded small but consistent gains, confirming that prompt sub-types exist, though gains were bounded by representation limits.

What Didn’t Work (Important)

  • KNN correction on embeddings → overfitting
  • Excessive feature stacking → diminishing returns
  • Over-calibration (isotonic, quantile) → hurt generalization
  • Deep meta-stacks → unstable CV

These experiments are documented to show what not to do.

Key Takeaways

  1. Structural signals matter, but are weak individually
  2. Ensembling provides large gains early, small gains later
  3. MoE helps only when experts see meaningfully different data
  4. Validation discipline is critical — many signals looked strong but failed on private LB
  5. Transformer-style token interactions were likely the missing inductive bias

Final Results

  • 🏆 Top-10 Private Leaderboard
  • 📉 Private MAE: 0.15016
  • 👥 Authors: MonarchOfCoding

Repository Structure