Perspective Transition of Large Language Models for Solving Subjective Tasks (2025)

Abstract: Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks. Different from objective tasks such as commonsense reasoning and arithmetic question-answering, the performance of LLMs on subjective tasks is still limited, where the perspective on the specific problem plays crucial roles for better interpreting the context and giving proper response. For example, in certain scenarios, LLMs may perform better when answering from an expert role perspective, potentially eliciting their relevant domain knowledge. In contrast, in some scenarios, LLMs may provide more accurate responses when answering from a third-person standpoint, enabling a more comprehensive understanding of the problem and potentially mitigating inherent biases. In this paper, we propose Reasoning through Perspective Transition (RPT), a method based on in-context learning that enables LLMs to dynamically select among direct, role, and third-person perspectives for the best way to solve corresponding subjective problem. Through extensive experiments on totally 12 subjective tasks by using both closed-source and open-source LLMs including GPT-4, GPT-3.5, Llama-3, and Qwen-2, our method outperforms widely used single fixed perspective based methods such as chain-of-thought prompting and expert prompting, highlights the intricate ways that LLMs can adapt their perspectives to provide nuanced and contextually appropriate responses for different problems.

## introduction large language models (llms) have exhibited substantial advancements @xcite identify whether the sentence "her smile was a blinding sun" is a paraphrase of the metaphoric sentence "her smile was wonderful and fulfilling to see". while both sentences express admiration for her smile, the second sentence lacks the vivid imagery and specific emotional intensity that the first sentence provides. thus, the second sentence is not an accurate paraphrase of the first. the answer is false. ## related work subjective tasks in nlp. compared with objective tasks such as commonsense reasoning @xcite and arithmetic question-answering @xcite , research on llms in subjective tasks (e.g., metaphor recognition and dark humor detection) @xcite @xcite is still underexplored. different from objective tasks that can often be clearly defined and solved, subjective tasks involve the capability to perceive context, language nuances, and emotions, which cannot be easily quantified or objectively measured, thereby posing challenges for current llms @xcite @xcite . for example, as shown in results of bigbench(bench authors, 2023), the zero-shot accuracy of palm-535b (chowdhery et al., 2023) model on metaphor recognition, dark humor detection, and sarcasm detection tasks does not exceed 50%. in-context learning of llms. as model parameters increase, llms gain stronger in-context learning @xcite , enhancing zero-shot and few-shot reasoning without fine-tuning. chain-of-thought (cot) prompting @xcite is widely used to elicit reasoning by adding explicit reasoning steps, emphasizing "how to think deeper" @xcite while neglecting "who should think." however, recent research @xcite suggests that such reasoning pathways are primarily effective for mathematical and symbolic reasoning. our work also relies on in-context learning, however, we propose a method based on dynamic perspective transition to elicit knowledge from the different perspective of llms, which does not rely on a single reasoning pathway and achieve better results on a wider range of subjective tasks. our work also relates to llmbased multi-agents @xcite , aiming to enhance llms' ability to understand context, analyze problems, and generate solutions beyond a single fixed perspective. perspective transition of llms. there are various ways to use llms currently that are based on different perspectives: 1) direct prompting methods @xcite @xcite let the model to provide answers based on the factual knowledge or reasoning ability by llms themselves directly, without setting specific roles. 2) by assigning roles @xcite @xcite such as experts and engaging in role-playing dialogue, the internal knowledge of llms on specific roles can be elicited. 3) by constructing scenarios through multiagent cooperation @xcite , debates @xcite , or dialogues @xcite , and then providing answers from a third-person perspective by incorporating contextualized information by the constructed agents. the previous methods only consider a fixed perspective and validate the effectiveness in certain problems. in contrast, rpt is orthogonal to improvements such as deep long-chain cot @xcite , enabling llms to adaptively select the most suitable perspective to solve various subjective tasks, which has not been studied in previous research. ## method the overall pipeline of the proposed rpt is structured into three steps, as shown in figure 3 . firstly, we input the task description and a specific question, prompting the model to select the most appropriate perspective for answering the question. secondly, the model evaluates and ranks these perspectives based on their confidence levels in addressing the question. thirdly, the model adopts the perspective with the highest confidence to formulate and deliver the definitive answer. illustration of the unified prompting and response /* input */ /* perspective-based instructions */ here are some examples of perspectives you can choose: direct perspective (answer the question directly). ....... role perspective (assume you are some roles (e.g., expert) and answer the question). ....... third-person perspective (simulate some agents and discussions, then summarize and give the answer).(d) ....... /* input question */ metaphor recognition: identify whether the sentence "adam did not understand the root of the crisis" is a paraphrase of the metaphoric sentence "adam did not understand the solution to the crisis" (q) firstly, analyzing the question from diverse perspectives, and selecting some available perspectives based on the question. (t 1 ) secondly, ranking all the perspectives according to the confidence level in solving the question. [note: the confidence indicates how likely you think your answer is true]. (t 2 ) finally, choosing the method with the highest confidence to solve the question, reason and give the final answer. select from true or false. (t 3 ) /* response of m */ perspective and confidence: (p, c) third-person perspective, 85% role perspective, 70% direct perspective, 60% selected perspective reasoning: (r) tom: adam did not understand the root of the crisis. jerry: so, does that mean adam did not understand the solution to the crisis? tom: not exactly. understanding the root of the crisis doesn't necessarily mean understanding the solution. understanding the root is figuring out what causes the problem, while a solution refers to ways of solving the problem. they can be related but are not synonymous. answer: false d and a specific question q, our goal is to let the llm m solve the question q.