  While large language models (LLMs) excel at understanding and generating
plain text, they are not specifically tailored to handle hierarchical text
structures. Extracting the task-desired property from their natural language
responses typically necessitates additional processing steps. In fact,
selectively comprehending the hierarchical structure of large-scale text is
pivotal to understanding its substance. Aligning LLMs more closely with the
classification or regression values of specific task through prompting also
remains challenging. To this end, we propose a novel framework called
Tree-Based Hard Attention with Self-Motivation for Large Language Models
(TEAROOM). TEAROOM incorporates a tree-based hard attention mechanism for LLMs
to process hierarchically structured text inputs. By leveraging prompting, it
enables a frozen LLM to selectively focus on relevant leaves in relation to the
root, generating a tailored symbolic representation of their relationship.
Moreover, TEAROOM comprises a self-motivation strategy for another LLM equipped
with a trainable adapter and a linear layer. The selected symbolic outcomes are
integrated into another prompt, along with the predictive value of the task. We
iteratively feed output values back into the prompt, enabling the trainable LLM
to progressively approximate the golden truth. TEAROOM outperforms existing
state-of-the-art methods in experimental evaluations across three benchmark
datasets, showing its effectiveness in estimating task-specific properties.
Through comprehensive experiments and analysis, we have validated the ability
of TEAROOM to gradually approach the underlying golden truth through multiple
inferences.
