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Beyond Attention—A Dive into the Interpretability of Self Attention Heads in modern LLMs
- Authors
- Name
- Rohith Shinoj Kumar
Introduction
Attention is All You Need—a paper that revolutionized everything we know today about large language models. It introduced the Transformer architecture and replaced recurrence with a powerful mechanism called self-attention, setting the stage for breakthroughs in NLP, from translation and summarization to code generation and reasoning. At the heart of these models lie hundreds, sometimes thousands, of attention heads—tiny components that decide what to focus on in each input.
As models grow in scale and capability, these attention heads are increasingly seen as the core of how LLMs "think" and "understand." But while we know that attention helps models weigh context, the inner workings of individual heads often remain a black box. Are they truly learning meaningful patterns? Do they specialize in certain tasks like grammar, coreference, or logic? Or are we just projecting structure onto something far more chaotic?
If attention is all we need—shouldn't we understand it better?
(If you are already familiar with Transformer architectures, feel free to skip to section 4 – Mechanistic Interpretability; the earlier sections intend to build context for what follows)
- When Did Language Models Get So “Large”?
- The Transformer Architecture (and why it matters)
- Attention Heads and Multi-Head Attention
- Mechnistic Interpretability: What are these heads really learning?
- The Limits of Knowing
When Did Language Models Get So “Large”?
Language models are far from new. In fact, the use of language models dates back decades, with early systems relying heavily on recurrent neural networks (RNNs) to process sequential data like text. RNNs were designed to handle sequences by maintaining a hidden state that carried information from previous words, making them natural candidates for language tasks.
So why did these tools fall short when it came to real-world usability? RNNs struggled with long-range dependencies, meaning they had difficulty remembering information from far back in a sentence or paragraph. This limitation made it challenging for them to capture the full context needed for complex language understanding. Moreover, RNNs processed data sequentially, which slowed down training and inference, making them inefficient for scaling up to the massive datasets and models we see today.
The boom in LLM usage we see today can be traced back to 2017, with the introduction of admittably the most groundbreaking neural architecture- the Transformer.
The Transformer Architecture (and why it matters)
The Transformer architecture, introduced in the 2017 paper Attention Is All You Need, marked a major turning point in the evolution of language models. Prior to this, most models relied on recurrence—specifically Recurrent Neural Networks (RNNs) or their more advanced cousins like LSTMs and GRUs—to process sequences word by word. While effective in some contexts, these models struggled with long-term dependencies, were slow to train due to their sequential nature, and often failed to capture broader context in a sentence or paragraph.
The Transformer flipped this approach entirely by introducing self-attention, a mechanism that allows the model to consider all words in a sentence at once. Rather than passing information from one word to the next step-by-step, the Transformer scans the entire input sequence in parallel and calculates how much attention each word should pay to every other word.
Hard to wrap your head around? Let’s consider the sentence:
“The trophy didn’t fit in the suitcase because it was too small.”
Now, ask yourself: What does "it" refer to? If you are a human (hopefully, because I was too lazy for CAPTCHA on this blog), you would quickly infer that it = the suitcase, was too small and not the trophy.
- RNN vs Transformer
An RNN reads the sentence one word at a time, updating its internal state as it goes:
"The" → "trophy" → "didn't" → "fit" → "in" → ...
Each word modifies the RNN’s hidden state, which carries forward some memory of what came before. But by the time it reaches the word "it, the information about "trophy" and "suitcase" might be diluted or forgotten. Mathematically speaking, the influence or percieved importance of the word trophy is negligibly small by the time the model has reached the word "it".
In contrast, Transformers process the entire sentence in parallel using self-attention, which means that when the model encounters the word “it”, it doesn’t just rely on the last few words—it looks back at every word in the sentence and asks: How relevant is this word to understanding “it”? At this point, the model may assign a softmax attention weight of lets say, 0.63 to the word "suitcase" and 0.3 (still relatively high in comparison to other words, but far lower than 0.63) to the word "trophy" because the phrase “too small” makes more semantic sense in relation to a suitcase than a trophy. In a way, this is exactly how the human brain thinks about this sentence, only with a touch of mathematics.
Attention Heads and Multi-Head Attention
Attention Heads are the foundational blocks of Transofrmer models in the same way bricks are to a wall. In its simplest form, each head decides which words in a sequence should pay attention to which other words. Each head thus performs its own version of:
“How much should token A care about token B?”
It takes a bunch of tokens and converts them into vector embeddings. Each head then computes attention scores—usually through the well-known Query, Key, and Value mechanism. Based on these scores, it blends information from other tokens into a context-aware representation of each word. Now, depending on how the attention mechanism is configured, Transformer models can be built using encoder-only, decoder-only, or encoder–decoder architectures.
Encoder-only models like BERT and RoBERTa use bidirectional self-attention, meaning each word can attend to all others in the input—great for tasks like classification or question answering. Decoder-only models like GPT, Claude, and LLaMA use causal (unidirectional) self-attention, where each word only attends to the ones before it, making them ideal for text generation. Finally, encoder–decoder models like T5, BART, and MarianMT combine both: the encoder reads the entire input using bidirectional attention, and the decoder generates output using causal self-attention while also attending to the encoder’s output via cross-attention—a powerful setup for tasks like translation or summarization.
One Head or Many? The Power of Multi-Head Attention
Language is complex. Consider the phrase:
“I saw the man with my binoculars. He was walking across the street.”
At first glance, it seems simple, but it can mean two very different things—did I use my binoculars to see the man, or does the man have my binoculars? Even humans get confused here, because the meaning changes entirely depending on how you interpret the syntax and context. How do we then expect a bunch of code and mathematics to interpret what we as (supposedly) superior beings get confused at.
Transformers don’t just rely on one attention head—they unleash a whole team working in parallel. Take GPT-3, for instance: it wields 96 attention heads, each zooming in on different clues in the text. GPT-4 cranks this up even more, packing in a larger number of heads to capture subtler nuances and deeper connections. This multi-head approach lets the model juggle syntax, meaning, and context all at once, blending these “mini-experts” into a larger unit that understands language with impressive depth and flexibility.
Sounds easy, right? “We have one that works, so why not add more? It should obviously work better!” But the reality is far more complex. Let’s take a deeper dive—albeit a very simplified one—into how multiple attention heads help models make sense of these subtle linguistic nuances.
Mechnistic Interpretability: What are these heads really learning?
One can guess, without loss of generality, that each head has a specific task that is later integrated across units. The more important question to be asked here is, are they just inscrutable black boxes that magically “understand” language by combining examples—or are there more concrete, interpretable mechanisms at play?
Simply put, each of these units focus on one very elementary task that makes little meaning when isolated but integrate to every nuance of language. These include but are not limited to:
1. Syntactic/Grammar Parsers
Some attention heads act like internal parsers. They focus solely on syntactic structures—linking subjects to verbs and maintaining grammatical agreement. For example, in GPT-like models, specific heads have been found that track subject–verb number agreement. When the sentence says “The dogs…”, one head ensures the verb is plural (“bark”), not singular (“barks”).
The figure above, derived from the paper 'A Multiscale Visualization of Attention in the Transformer Model' by Vig et. al tracks lexical patterns in Pythia/GPT heads in GPT-2. Each panel plots one head’s attention (colored lines) for an input sentence. The left head (layer 8) copies each list item to itself (“apples, oranges, bananas”); the center head (layer 3) couples verbs (“ran”, “barked”); the right head (layer 10) connects letters of the acronym “ASA”.
Other heads follow sentence flow, identifying list structures, acronyms, or verbs—like internal pattern matchers that give structure to otherwise flat sequences of tokens.
2. Induction based Pattern Matching
Another discovery was the Induction Circuit heads-—a mini-program embedded in multiple attention heads that learns to detect and repeat token patterns For example, if a prompt says
“Alice, Bob, Alice”
the model learns to predict “Bob” next—not because it understands names, but because induction heads matched the repeated “Alice” and inferred the paired continuation. Most intriguingly, this mechanism is surprisingly algorithmic-kind of like a for loop written in neural weights
3. Entity Tracking Heads
Known as Name Moves or Coreference Heads, these specialize in tracking entities across long passages. For example, in a sentence like “The nurse saw the patient. She took notes.” it is these heads that understand “She” refers to the nurse.
Interestingly, there are even inhibition heads that block attention to incorrect entities. For example, if two names are mentioned, these heads actively prevent the model from choosing the wrong one.
4. Indirect Object Identification (IoI) tasks
For example, in the sentence "Alice recieved the notes sent by Bob and Alice completed the assignment", these heads flag repeated tokens (Alice) and steer the model toward the relevant instance.5. Redundant Circuits
It is deeply rooted in human nature to sense a chance of failure and come up with backup ideas-the philosophy of There's always a Plan-B. It may turn out that this is, infact, not a human emotion at all. Transformer models often contain multiple redundant circuits: units that perform the same task in different ways. If one fails or is ablated (washed out), others take over. This reveals a robust, distributed architecture. Heads aren’t just independent filters—they form interconnected webs that back each other up like a neural fallback system.
The Limits of Knowing
While we’ve explored the many roles multi-head attention units can play, it’s important to note that these interpretations are simplifications. Interpretability remains one of the most difficult and nuanced challenges in deep learning. Some heads don’t even deal with language in the usual sense. In models like Code LLaMA, certain heads act more like inbuilt calculators—performing transformations on internal vectors, rather than extracting linguistic meaning.
Broadly speaking, not every head is a language expert—some are internal calculators, some may be silent orchestrators, and others might just be ghosts—present, but whose purpose we’ve yet to decipher. We may never fully understand what all attention heads do in large models, and thats okay!. Nonetheless, it would not be possible to optimize these models without understanding what happens in the black-box we call AI, and this becomes even harder as language models become larger and increasingly multimodal-able to see, hear, and reason across domains.