Verifying LiteCoST Generalization to Additional Domains

Establish whether the LiteCoST framework—comprising Chain-of-Structured-Thought prompting and GRPO fine-tuning of small language models for long-document question answering—generalizes to domains beyond finance, legal, and open-domain QA by rigorously evaluating its performance on additional domain-specific long-document QA datasets and verifying that its structured outputs and downstream answers remain accurate and reliable across these distinct domains.

Background

LiteCoST is proposed as a two-pillar approach that first uses strong LLMs to generate Chain-of-Structured-Thought traces and structured outputs, and then distills this behavior into compact small LLMs via SFT and GRPO. Experiments demonstrate strong results in finance, legal, and open-domain settings, with efficiency advantages over large models.

The authors explicitly acknowledge a remaining uncertainty regarding how well LiteCoST transfers to other distinct domains. They attribute part of the challenge to the scarcity of suitable domain-specific long-document QA datasets, indicating a need for further empirical verification of cross-domain generalization.