Navigating Opinion Space: A Study of Explicit and Implicit Opinion Generation in Language Models (2024)

Abstract: The paper focuses on testing the use of conversational Large Language Models (LLMs), in particular chatGPT and Google models, instructed to assume the role of linguistics experts to produce opinionated texts, which are defined as subjective statements about animates, things, events or properties, in contrast to knowledge/evidence-based objective factual statements. The taxonomy differentiates between Explicit (Direct or Indirect), and Implicit opinionated texts, further distinguishing between positive and negative, ambiguous, or balanced opinions. Examples of opinionated texts and instances of explicit opinion-marking discourse markers (words and phrases) we identified, as well as instances of opinion-marking mental verbs, evaluative and emotion phraseology, and expressive lexis, were provided in a series of prompts. The model demonstrated accurate identification of Direct and Indirect Explicit opinionated utterances, successfully classifying them according to language-specific properties, while less effective performance was observed for prompts requesting illustrations for Implicitly opinionated texts.To tackle this obstacle, the Chain-of-Thoughts methodology was used. Requested to convert the erroneously recognized opinion instances into factual knowledge sentences, LLMs effectively transformed texts containing explicit markers of opinion. However, the ability to transform Explicit Indirect, and Implicit opinionated texts into factual statements is lacking. This finding is interesting as, while the LLM is supposed to give a linguistic statement with factual information, it might be unaware of implicit opinionated content. Our experiment with the LLMs presents novel prospects for the field of linguistics.

## introduction the present paper aims to discuss testing results with reference to the use of conversational large language model (llm), in particular chatgpt and google models, instructed to assume the role of linguistics expert in our testing exercises. ## language of opinions language of opinionated texts is characterized by some properties which make it potentially distinguishable from fact-based statements. on the other hand, the class of implicit opinionated texts is particularly problematic to identify outside of context, because of the absence of a set of criterial, ever present universal markers. therefore, we assume that some of the tasks to identify this and some other categories of opinion text, will also cause identification and illustration problems for llm models. ## discussion it is important to note that llms are nondeterministic models, meaning they exhibit different behaviors each time they are executed. executing the identical prompt can yield varying instances. in addition, we did not optimize the prompts for any of the models. customizing the prompt for each llm individually may lead to improved outcomes. however, provision of more detailed, contextually specific condition types for each possible implicit opinion scenario might be considered time and effort consuming, especially when a series of similar forms would need to be sequentially generated, with reference to distinct objects or domains. a cumulative prompt admitting variance would be needed in such cases which would constitute an adequate prompting signal for the system. this level of opinion analysis is typically influenced by the context around the sentence and is considered decisive for applications dealing with various types of text -from social networks, political comments and casual conversations. its aim is to determine language users' opinions about various phenomena, people, objects, properties and states and possibly confront them with the actual states of affairs in the outside reality. in order to perform this level of analysis, it is essential to identify both the linguistic context and the actual situational scene entities mentioned and their respective properties related to the words produced. from there, the judgment concerning a distinction between factual