Papers by Soham Shah

2 papers
Select-then-Route : Taxonomy guided Routing for LLMs (2025.emnlp-industry)

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Challenge: Large language models have boosted performance across a broad spectrum of tasks . sending each query to the most suitable model is prohibitively expensive .
Approach: They propose a framework that selects a small pool of LLMs and routes queries through an adaptive cascade.
Outcome: The proposed framework improves accuracy and latency by 4X while reducing inference cost.
Distilling Script Knowledge from Large Language Models for Constrained Language Planning (2023.acl-long)

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Challenge: Existing work exploits language models to plan for abstract goals of stereotypical activities, but leaves more specific goals with multi-facet constraints understudied.
Approach: They propose an over-generate-then-filter approach to improve large language models on constrained language planning task by distilling a constrained script dataset.
Outcome: The proposed approach improves the constrained language planning ability of large language models on constraint faithfulness and also in smaller LMs.

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