Work

TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination

Year: 2026

Type: conference-paper

Abstract: 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.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.1136

Authors: Omar Naim, Krish Sharma, Niyar R Barman, Nicholas Asher

Institutions: Institut de Recherche en Informatique de Toulouse, Université de Toulouse, Centre National de la Recherche Scientifique

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Agence Nationale de la Recherche

Awards: G3010527862, G3778207229, G3879958799, G602161992, Distant Supervision for Meeting Minutes with Rhetorical Relations