Unveiling Instruction-Specific Neurons & Experts: An Analytical
  Framework for LLM's Instruction-Following Capabilities
The finetuning of Large Language Models (LLMs) has significantly advanced
their instruction-following capabilities, yet the underlying computational
mechanisms driving these improvements remain poorly understood. This study
systematically examines how fine-tuning reconfigures LLM computations by
isolating and analyzing instruction-specific sparse components, i.e., neurons
in dense models and both neurons and experts in Mixture-of-Experts (MoE)
architectures. In particular, we introduce HexaInst, a carefully curated and
balanced instructional dataset spanning six distinct categories, and propose
SPARCOM, a novel analytical framework comprising three key contributions: (1) a
method for identifying these sparse components, (2) an evaluation of their
functional generality and uniqueness, and (3) a systematic comparison of their
alterations. Through experiments, we demonstrate functional generality,
uniqueness, and the critical role of these components in instruction execution.
By elucidating the relationship between fine-tuning-induced adaptations and
sparse computational substrates, this work provides deeper insights into how
LLMs internalize instruction-following behavior for the trustworthy LLM
community.
6837eb38f09a146728a4b80f
Junyan Zhang
6837eb38f09a146728a4b810
Yubo Gao
6837eb38f09a146728a4b811
Yibo Yan
6837eb38f09a146728a4b812
Jungang Li
6837eb38f09a146728a4b813
Zhaorui Hou
6837eb38f09a146728a4b814
Sicheng Tao
6837eb38f09a146728a4b815
Shuliang Liu
6837eb38f09a146728a4b816
Song Dai
6837eb38f09a146728a4b817
Yonghua Hei
6837eb38f09a146728a4b818
Junzhuo Li
6837eb38f09a146728a4b819
Xuming Hu
Unveiling Instruction-Specific Neurons & Experts: An Analytical
  Framework for LLM's Instruction-Following Capabilities
The finetuning of Large Language Models (LLMs) has significantly advanced
their instruction-following capabilities, yet the underlying computational
mechanisms driving these improvements remain poorly understood. This study
systematically examines how fine-tuning reconfigures LLM computations by
isolating and analyzing instruction-specific sparse components, i.e., neurons
in dense models and both neurons and experts in Mixture-of-Experts (MoE)
architectures. In particular, we introduce HexaInst, a carefully curated and
balanced instructional dataset spanning six distinct categories, and propose
SPARCOM, a novel analytical framework comprising three key contributions: (1) a
method for identifying these sparse components, (2) an evaluation of their
functional generality and uniqueness, and (3) a systematic comparison of their
alterations. Through experiments, we demonstrate functional generality,
uniqueness, and the critical role of these components in instruction execution.
By elucidating the relationship between fine-tuning-induced adaptations and
sparse computational substrates, this work provides deeper insights into how
LLMs internalize instruction-following behavior for the trustworthy LLM
community.
The study investigates the role of sparse computational components in the instruction-following capabilities of Large Language Models through systematic analysis and introduces HexaInst and SPARCOM for better understanding.
Large Language Models fine-tuning instruction-following HexaInst SPARCOM sparse components neurons Mixture-of-Experts instruction execution computational adaptations