Boosting Spoken Language Understanding via Large Language Models
Authors: Xuxin Cheng, Xianwei Zhuang, Zhihong Zhu, Wanshi Xu, Yaowei Li, Yuexian Zou


Reviewer paper summaries: This paper proposes LA-SLU (LLM Assisted SLU), a model that uses instruction-tuning with PEFT to adapt an LLM (Llama 2) to the task of multi intent spoken language understanding.

The authors correctly point out how current LLM-based models do already perform very well in single intent settings, but often fail to perform as well in multi intent scenarios.

To better utilise the capabilities of LLMs, the authors proceed to turn multi intent datasets into data suitable for instruction tuning, and tune a Llama 2 via LoRA for the task.

Experiments show that their proposed LA-SLU can outperform current SOTA systems on the MixATIS and MixSNIPS datasets; ablations indicate that the individual contributions are meaningful, and contribute to the overall performance of LA-SLU.
Summary of strengths: - paper is easy to ready and understand
- the proposed approach is straight-forward and effective
Summary of weaknesses: little novelty in the approach: CoT has been done and demonstrated to be effective on multiple tasks; instruction tuning (including transforming datasets into IT data) has been done and shown to be effective on multiple tasks; using PEFT for instruction tuning of specific tasks has been done, and shown to be effective on multiple tasks.
The only real novelty here seems to be using this for multi intent SLU.
Comments, suggestions and typos: No comments, all is clear about methods; it is just not clear to me that this whole approach is particularly novel (see weaknesses).

Lines 95-96: "In this paper, we present an innocent approach for predicting intents" - what is an 'innocent approach'?
Overall assessment scores: 3