Google DeepMind Research
Just-in-time and distributed task representations in language models
Language models' impressive capabilities originate from their in-context learning: based on instructions or examples, they can infer and perform new tasks without weight updates. This work investigates when representations for new tasks are formed and how they change over context, focusing on 'transferrable' task representations capable of restoring task context.
Executive Impact Summary
This research from Google DeepMind unveils the intricate dynamics of In-Context Learning (ICL) in language models. We demonstrate that while high-level task identity persists, the *transferrable* representations critical for task performance are formed 'just-in-time' and exhibit strong locality, influencing how enterprises can best leverage and understand advanced LLM behaviors.
⚡ Just-in-Time
Representation Activation
🧩 Minimal Scope
Task Context Capture
📈 Enhanced
Performance Accrual
👁️ Persistent
Task Identity Signal
Deep Analysis & Enterprise Applications
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Sporadic Activation of Transferrable Task Representations
The study reveals that transferrable task representations in language models activate in a surprisingly non-gradual and sporadic manner, forming only at certain key tokens within the context (Figure 1C, 2B). This contrasts with a more continuous, inert representation of high-level task identity that persists throughout the context, suggesting a 'just-in-time' computational process for dynamic adaptation.
Just-in-Time Activation of Transferrable RepresentationsTask Context Decay Over Longer Generation
Reinstantiated task contexts for longer- and mixed-generation tasks often decay over time, meaning their ability to guide model behavior diminishes over longer generation sequences and across independent subtask contexts (Figure 4). This indicates a strong temporal locality in how these representations influence output.
Short-Lived Effective Task ContextMinimal Task Scope Capturing
Models tend to form local task representations that capture a 'minimal task scope,' such as a semantically-independent subtask. For instance, in tasks like 'CHOOSE_FIRST_MIDDLE_LAST_OF_5', the reinstated task context might only support generating the first word, requiring other mechanisms for subsequent tokens (Section 3.3).
Minimal Scope Captured by Local RepresentationsTask Decomposition Process
The research suggests language models engage in a form of 'task decomposition.' For composite tasks, they segment semantically-independent scopes, offloading parts of the task representation to multiple tokens rather than condensing it all into a single local representation. This is particularly evident in mixed-generation tasks where only the first subtask is effectively encapsulated by initial representations (Section 3.3).
Identify Task Goal
→
Decompose into Subtasks
→
Generate First Subtask Output
→
Re-evaluate Context for Next Subtask
→
Generate Subsequent Outputs
Evidence Condensation & Performance Alignment
Transferrable task representations condense evidence from multiple examples in context, and this accrual aligns well with behavioral performance improvements (Figure 2A). As models gain more examples, the representations become better at restoring task context, leading to higher zero-shot accuracy when injected.
High Fidelity Evidence Accrual Improves Performance
↓ Variance
Across Examples (Figure 3A)
↓ L2 Norm
Over More Examples (Figure 3A)
Complex Task Challenges: State Tracking
For certain complex tasks requiring more State Tracking (e.g., COUNT_COLOR_IN_3, COUNT_FRUIT_IN_3), local task representations (extracted from the last token) were not able to leverage more examples for transfer. This suggests that models may not condense all necessary inference processes for such tasks into simple, local representations, potentially requiring distributed representations or more complex interventions.
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