SWI: Speaking with Intent in Large Language Models
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.
67e6069f19f7dbaac899dc4f
Yuwei Yin
claimed_verified
2025-03-28T08:37:44.456Z
64510a21f800611f94f0d9f8
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/lOeHK9Bvt3IXcB7Urx6jZ.jpeg
Yuwei Yin
user
yuweiyin
67e6069f19f7dbaac899dc50
EunJeong Hwang
67e6069f19f7dbaac899dc51
Giuseppe Carenini
SWI: Speaking with Intent in Large Language Models
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.
Speaking with Intent, a novel method in LLMs, enhances reasoning and generation quality by incorporating explicit intent, outperforming other prompting methods across various benchmarks.
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