Probing the Attention Representation of Filler-Gap Dependency in Transformers

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Attention probing experiments on GPT-2 identified two specific attention heads (layer 5, head 2, and layer 8, head 9) whose verb-to-filler attention correlates with filled-gap surprisal. Prior work showed neural language models exhibit filled-gap effects that attenuate with intervening clauses, especially with overt complementizers. These two identified heads are sensitive to clausal intervention but not linear distance, and they display distinct patterns in islands. Notably, when intervening overt complementizers appear, head 2 of layer 5's attention redistributes from the filler to the nearest complementizer, exhibiting an "attend-closest-C" pattern, while head 9 of layer 8 does not. These findings suggest LMs may allocate distinct linguistically meaningful representations to individual attention heads but fail to fully learn the correct grammars of Filler-Gap Dependencies (FGDs).

Key takeaway

For NLP Engineers developing or fine-tuning Transformer models for complex syntactic tasks, understanding how specific attention heads process filler-gap dependencies is crucial. Your diagnostic efforts should include attention probing to identify heads exhibiting incomplete grammatical learning, like the "attend-closest-C" pattern observed in GPT-2. This can guide targeted architectural adjustments or training data augmentation to improve syntactic robustness and ensure more complete grammatical acquisition in your models.

Key insights

Specific GPT-2 attention heads correlate with filler-gap surprisal but show incomplete grammatical learning.

Principles

• Attention heads can encode specific linguistic phenomena.
• Clausal intervention impacts attention patterns more than linear distance.
• LMs may allocate meaningful representations but fail full grammar.

Method

Attention probing experiments on GPT-2 to identify heads correlating verb-to-filler attention with filled-gap surprisal, analyzing sensitivity to clausal intervention and island effects.

In practice

• Analyze specific attention heads for linguistic feature encoding.
• Investigate intervention effects on model's syntactic processing.
• Target specific heads for improving grammatical understanding.

Topics

• Transformer Models
• Attention Mechanisms
• Filler-Gap Dependencies
• GPT-2
• Linguistic Probing
• Syntactic Analysis

Best for: Research Scientist, AI Scientist, NLP Engineer

Related on AIssential

• How Transformers See Language: Multi-Head Attention and Positional Encoding Explained — Multi-Head Attention and Positional Encoding are fundamental to Transformers' ability to understand language context and order.
• Decoder only Transformer : Building a GPT-2 model prototype to make it understand Natural Language… — Training a GPT-2 prototype on classic novels demonstrates decoder-only Transformer architecture and performance benefits of pre-trained weights.
• AttentionApp: An Interactive Tool for Analyzing Transformer Attention Patterns in Portuguese — AttentionApp provides an interactive way to visualize and analyze Transformer attention patterns in Portuguese.
• Disentangling Direction and Magnitude in Transformer Representations: A Double Dissociation Through L2-Matched Perturbation Analysis — Direction and magnitude of Transformer hidden states have distinct computational roles in LayerNorm-based architectures.
• The Flow of Attention — The Transformer's attention mechanism reconfigures token embeddings as a coupled particle flow in Wasserstein space, organizing contextual meaning.
• Repo for implementations of various Transformer Attn mechanisms [P] — The "attnhut" GitHub repo offers diverse Transformer attention mechanisms for SLM, CV, and RL experiments.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.