Progressive Tuning: Towards Generic Sentiment Abilities for Large Language Models (2024)

Abstract: Understanding sentiment is arguably an advanced and important capability of AI agents in the physical world. In previous works, many efforts have been devoted to individual sentiment subtasks, without considering interrelated sentiment knowledge among these subtasks. Although some recent works model multiple sentiment subtasks in a unified manner, they merely simply combine these subtasks without deeply exploring the hierarchical relationships among subtasks. In this paper, we introduce GSA-7B, an open-source large language model specific to the sentiment domain. Specifically, we deeply explore the hierarchical relationships between sentiment subtasks, proposing progressive sentiment reasoning benchmark and progressive task instructions. Subsequently, we use Llama2-7B as the backbone model and propose parameter-efficient progressive tuning paradigm which is implemented by constructing chain of LoRA, resulting in the creation of GSA-7B. Experimental results show that GSA-7B as a unified model performs well across all datasets in the progressive sentiment reasoning benchmark. Additionally, under the few-shot setting, GSA-7B also exhibits good generalization ability for sentiment subtasks and datasets that were not encountered during its training phase.

## introduction sentiment plays a crucial role in social interaction. minsky, in his book "society of mind" @xcite , mentions that equipping machines with the ability to understand the sentiment across different scenarios has consistently been the steadfast goal of researchers. with large language models (llms) playing a growing role in our lives, developing llms with sentiment intelligence could be better at communicating with us, collaborating with us, and understanding us @xcite . ## progressive sentiment reasoning benchmark as shown in figure 1 , different sentiment subtasks form a progressive reasoning chain, leading to a renewed understanding of each sentiment subtask from a reasoning perspective. this section provides a description of the datasets that constitute the sentiment reasoning benchmark. more details can be found in level 1: sentiment analysis sst-2 @xcite , mosi @xcite and mo-sei @xcite are three widely used datasets for sentiment analysis focusing on sentiment polarity at the sentence (single-turn utterance) level. for the mosi and mosei datasets, we only use their textual modal information. level 2: emotion recognition in conversation iemocap @xcite , meld @xcite and emorynlp (zahiri and choi, 2018) are three widely used datasets for emotion recognition in conversation, which aims to identify emotions conveyed in each utterance within the dialogue context @xcite . for the iemocap and meld datasets, we only use their textual modal information. level 3: sarcasm detection mustard @xcite and mustard++ @xcite are two widely used datasets for sarcasm detection in a conversational setting, which involves finding linguistic expression and emotional states incongruity. for the mustard and mustard++ datasets, we only use their textual modal information. ## progressive tuning in this section, we first introduce the task instruction used for progressive tuning, followed by a detailed description of the progressive tuning process. ## conclusion in this paper, we deeply explore the hierarchical relationships between sentiment subtasks, proposing progressive reasoning benchmark that includes sa (level 1), erc (level 2), and sd (level 3) tasks and progressive task instruction. subsequently, we propose parameter-efficient progressive tuning paradigm to finetune the llama2-7b model. the resulting model is named gsa-7b, a specialized open-source llm for the sentiment domain. experimental results show that gsa-7b performs well across all datasets in the progressive sentiment reasoning benchmark and exhibits good generalization ability for sentiment subtasks and datasets that were not encountered during its training phase, which reveals the feasibility of building ai that can understand sentiment.