title: SWI: Speaking with Intent in Large Language Models
summary: Intent, typically clearly formulated and planned, functions as a cognitive
  framework for reasoning 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 communication. 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 mathematical reasoning benchmarks consistently
  demonstrate the superiority of Speaking with Intent over Baseline (i.e.,
  generation without explicit intent). Moreover, SWI outperforms answer-trigger
  prompting methods Chain-of-Thought and Plan-and-Solve and maintains competitive
  performance with the strong method ARR (Analyzing, Retrieving, and Reasoning).
  Additionally, the effectiveness and generalizability of SWI are solidified on
  reasoning-intensive question answering (QA) and text summarization benchmarks,
  where SWI brings consistent improvement to the Baseline generation. In text
  summarization, SWI-generated summaries exhibit greater accuracy, conciseness,
  and factual correctness, with fewer hallucinations. Furthermore, human
  evaluations verify the coherence, effectiveness, and interpretability of the
  intent produced by SWI. This proof-of-concept study creates a novel avenue for
  enhancing LLMs' reasoning abilities with cognitive notions.
paper_authors._id: 67e6069f19f7dbaac899dc4f
paper_authors.name: Yuwei Yin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-28T08:37:44.456Z
paper_authors._id: 64510a21f800611f94f0d9f8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/lOeHK9Bvt3IXcB7Urx6jZ.jpeg
paper_authors.fullname: Yuwei Yin
paper_authors.type: user
paper_authors.user: yuweiyin
paper_authors._id: 67e6069f19f7dbaac899dc50
paper_authors.name: EunJeong Hwang
paper_authors._id: 67e6069f19f7dbaac899dc51
paper_authors.name: Giuseppe Carenini
paper_title: SWI: Speaking with Intent in Large Language Models
paper_summary: Intent, typically clearly formulated and planned, functions as a cognitive
  framework for reasoning 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 communication. 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 mathematical reasoning benchmarks consistently
  demonstrate the superiority of Speaking with Intent over Baseline (i.e.,
  generation without explicit intent). Moreover, SWI outperforms answer-trigger
  prompting methods Chain-of-Thought and Plan-and-Solve and maintains competitive
  performance with the strong method ARR (Analyzing, Retrieving, and Reasoning).
  Additionally, the effectiveness and generalizability of SWI are solidified on
  reasoning-intensive question answering (QA) and text summarization benchmarks,
  where SWI brings consistent improvement to the Baseline generation. In text
  summarization, SWI-generated summaries exhibit greater accuracy, conciseness,
  and factual correctness, with fewer hallucinations. Furthermore, human
  evaluations verify the coherence, effectiveness, and interpretability of the
  intent produced by SWI. This proof-of-concept study creates a novel avenue for
  enhancing LLMs' reasoning abilities with cognitive notions.
paper_ai_summary: Speaking with Intent, a novel method in LLMs, enhances reasoning and generation quality by incorporating explicit intent, outperforming other prompting methods across various benchmarks.
paper_ai_keywords: Speaking with Intent large language models explicit intent deliberate and purposeful thoughts Chain-of-Thought Plan-and-Solve ARR reasoning-intensive question answering text summarization accuracy conciseness factual correctness hallucinations coherence interpretability