Summary of Verbosity-aware Rationale Reduction: Effective Reduction Of Redundant Rationale Via Principled Criteria, by Joonwon Jang et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI)

Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant Rationale via Principled Criteria

by Joonwon Jang, Jaehee Kim, Wonbin Kweon, Hwanjo Yu

First submitted to arxiv on: 30 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed novel sentence-level rationale reduction training framework leverages likelihood-based criteria to identify and remove redundant reasoning sentences, maintaining model performance while reducing generation length by 17.15% on average across various models and tasks.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper proposes a new way to improve large language models (LLMs) without making them work too hard. Right now, LLMs generate lots of extra information to help them answer tricky questions better. But this makes them slower and more expensive to use. The new method finds and gets rid of unnecessary steps in the thinking process, keeping the model’s quality but cutting down on how much it needs to do.