TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914550155051008 |
|---|---|
| author | Naim, Omar Sharma, Krish Barman, Niyar R Asher, Nicholas |
| author_facet | Naim, Omar Sharma, Krish Barman, Niyar R Asher, Nicholas |
| contents | Large Language Models (LLMs) typically come with a fixed architecture, despite growing evidence that not all layers contribute equally to every downstream task. We introduce TALE (Task-Aware Layer Elimination), an inference-time method that improves task performance by selectively removing layers that are irrelevant or detrimental for a given task. TALE optimizes task-specific performance, yielding a task-optimized architecture without retraining. Across 9 tasks and 5 model families, under both zero-shot and few-shot settings, TALE consistently matches or surpasses baseline performance while simultaneously reducing computational costs. TALE also synergizes with fine-tuning, leading to further performance improvements. Computing TALE for a new task requires modest resources, making it a practical and deployable solution for task-specialized LLM inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination Naim, Omar Sharma, Krish Barman, Niyar R Asher, Nicholas Machine Learning Computation and Language Large Language Models (LLMs) typically come with a fixed architecture, despite growing evidence that not all layers contribute equally to every downstream task. We introduce TALE (Task-Aware Layer Elimination), an inference-time method that improves task performance by selectively removing layers that are irrelevant or detrimental for a given task. TALE optimizes task-specific performance, yielding a task-optimized architecture without retraining. Across 9 tasks and 5 model families, under both zero-shot and few-shot settings, TALE consistently matches or surpasses baseline performance while simultaneously reducing computational costs. TALE also synergizes with fine-tuning, leading to further performance improvements. Computing TALE for a new task requires modest resources, making it a practical and deployable solution for task-specialized LLM inference. |
| title | TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2510.22767 |