SWI : Speaking with Intent in Large Language Models (2025)

Abstract: Intent, typically clearly formulated and planned, functions as a cognitive framework for communication and problem-solving. This paper introduces the concept of Speaking with Intent (SWI) in large language models (LLMs), where the explicitly generated intent encapsulates the model’s underlying intention and provides high-level planning to guide subsequent analysis and action. By emulating deliberate and purposeful thoughts in the human mind, SWI is hypothesized to enhance the reasoning capabilities and generation quality of LLMs. Extensive experiments on text summarization, multi-task question answering, and mathematical reasoning benchmarks consistently demonstrate the effectiveness and generalizability of Speaking with Intent over direct generation without explicit intent. Further analysis corroborates the generalizability of SWI under different experimental settings. Moreover, human evaluations verify the coherence, effectiveness, and interpretability of the intent produced by SWI. The promising results in enhancing LLMs with explicit intents pave a new avenue for boosting LLMs’ generation and reasoning abilities with cognitive notions.

## introduction intent, the goal-oriented intention in our mind @xcite @xcite , serves as a critical component in communication and a guiding framework for problem-solving. as illustrated in figure 1(a) , human thinking @xcite typically follows a structured loop where intent-a mental state or proactive commitment to perform a specific action or produce a particular outcome-directs problem analysis and logical reasoning, therefore also facilitating communication and interaction. hence, we hypothesize that enabling ai systems to speak with their intent explicitly can replicate this meta-cognitive process, thereby improving their generation quality and reasoning ability. in recent years, large language models (llms) @xcite @xcite have revolutionized natural language processing (nlp) with their excellent generative capabilities @xcite @xcite . enhancing llms on various language understanding and logical reasoning tasks @xcite @xcite is vital for their ongoing development @xcite @xcite . this work introduces speaking with intent (swi), requiring llms to articulate their own intent as a planning mechanism during generation, which makes llm intentionality more explicit, in a way reminiscent of the long tradition of intentdriven generation in classic nlg @xcite @xcite . in essence, we hypothesize that, due to the autoregressive nature of llms @xcite and the attention mechanism @xcite , explicitly stated intent provides high-level guidance for subsequent analysis and reasoning. for example, when applying swi to summarization tasks, each analytical step in summarizing an article is effectively guided by a preceding intent statement, which is a piece of free-form text generated by instruction-following llms @xcite @xcite instead of a predefined class as in traditional intent modeling @xcite . in this work, we verify the proposed hypothesis by comprehensively evaluating the effectiveness and generalizability of the proposed swi method. specifically, the experimental results across three diverse task categories (i.e., text summarization, multi-task question answering, and mathematical reasoning) demonstrate that speaking with intent in llms consistently outperforms directly generating responses without explicit intent. in summarization tasks, swi produces summaries that are more accurate, concise, and factually reliable, with fewer hallucinations @xcite in the output. in math reasoning tasks, swi surpasses the llm reasoning method chain-of-thought (cot) @xcite and llm planning method plan-and-solve (ps) @xcite , and swi can work synergistically with these methods to further improve them. additionally, we perform human evaluations to assess the coherence, effectiveness, and interpretability of the intent generated by our swi method. evaluators largely agree on the quality of the generated intent, particularly for mathematical reasoning tasks. the evaluation results confirm that swi enhances task performance and output explainability. the key contributions of this work are as follows: ➊ we introduce speaking with intent in llms, where the generated intent effectively guides problem analysis, logical reasoning, and text generation, boosting performance across various benchmarks. ➋ extensive experiments and analyses across diverse task types and multiple datasets, including text summarization, multi-task qa, and mathematical reasoning, demonstrate the consistent effectiveness and generalizability of swi. ➌ human evaluations validate the coherence, effectiveness, and interpretability of the intent generated by swi, with our evaluation practice providing standards for assessing freely generated intents. ## speaking with intent this section presents the problem-solving workflow of llms and introduces speaking with intent (swi), enabling llms to explicitly articulate their intent during response generation. ## experimental setup this section presents the experimental setup, including tasks and datasets ( § 3.1), model settings ( § 3.2), and baseline settings ( § 3.3). ## main results this section presents the experimental results to verify the effectiveness of swi on diverse generation and reasoning tasks. ## related work intent in nlg and llms. since the seminal work @xcite , intent has played a critical role in nlg. in the classical nlg pipeline @xcite , content determination and document planning are modeled as a process of communicative goals decomposition and ordering, where the resulting planned communicative acts the nlg system wants to achieve are its intentions. most approaches followed this framework by implementing a computational model of the rhetorical structure theory (rst) @xcite , a discourse theory that explains how parts of a multi-sentential text relate to each other functionally, i.e., how each piece serves a communicative purpose relative to the whole. for instance, rst has been applied in nlg to many genres, ranging from handling explanation dialogues @xcite to generating persuasive evaluative arguments @xcite . in all these applications, the text was planned before being generated and intentions were explicit. in contrast, in modern llm-driven generation, intent is typically implicit. in other words, what we see is only the generated text, with no access to the underlying communicative goals and corresponding intentions. in this respect, swi can be seen as the first attempt to make llm intentionality more explicit, bridging the gap between classic nlg and llms, arguably boosting llms' controllability and interpretability. llm reasoning. early llms were rather poor at reasoning @xcite , and scaling pre-training was shown not to be a feasible solution for improving reasoning @xcite . instead, chain-of-thought (cot) prompting @xcite demonstrated that by modifying the prompt, llms can elicit a beneficial step-by-step reasoning process at test time without additional training @xcite @xcite . building on cot, various reasoning techniques have emerged @xcite . among them, a recent research arr @xcite consistently outperforms cot on multiple qa tasks, where analyzing the intent of questions is its most effective component. different from arr, we enable llms to articulate their intent, using it to guide subsequent analysis and reasoning for improved task performance. inspired by the success of cot and similar prompting techniques, very recent research is increasingly focusing on enhancing llms reasoning abilities by explicitly training them for reasoning using reinforcement learning (rl) @xcite . intriguingly, the success of swi demonstrated in this paper could spur a similar explosion of research on training llms with rl @xcite to better analyze and formulate intentions. intent-related research. intent detection (id) and new intent discovery (nid) @xcite @xcite @xcite @xcite , which classify utterances into known or novel intent categories, are longstanding challenges in natural language understanding @xcite @xcite . typically, these tasks are approached as classification problems @xcite @xcite , where models assign sentences to predefined intent classes. on the contrary, our swi method generates intent as free-form text rather than fixed categories, enhancing flexibility and fluency. swi naturally integrates intent statements as planning into the reasoning process, providing contextual guidance for subsequent analysis. ## conclusion in this work, we introduce speaking with intent (swi) in llms, where the generated intent (as high-level planning) guides subsequent analysis, improving the generation and reasoning abilities. extensive experiments across text summarization, multi-task qa, and mathematical reasoning benchmarks consistently show the benefits of speaking with explicit intent over the direct generation baseline. in text summarization, swi produces summaries that are more accurate, concise, and factually reliable, with fewer hallucinations. in addition, swi outperforms existing llm reasoning and planing methods and works synergistically with them. further analysis substantiates the generalizability of swi when applied to different settings. moreover, human evaluations solidify the coherence, effectiveness, and interpretability of llm-generated intent. overall, this study opens a new avenue for enhancing llm generation and reasoning abilities.