LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play
- URL: http://arxiv.org/abs/2405.06373v4
- Date: Thu, 8 Aug 2024 04:47:20 GMT
- Title: LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play
- Authors: Li-Chun Lu, Shou-Jen Chen, Tsung-Min Pai, Chan-Hung Yu, Hung-yi Lee, Shao-Hua Sun,
- Abstract summary: Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended questions.
We propose LLM Discussion, a three-phase discussion framework that facilitates vigorous and diverging idea exchanges.
We evaluate the efficacy of the proposed framework with the Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test.
- Score: 43.55248812883912
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended questions. To enhance LLM creativity, our key insight is to emulate the human process of inducing collective creativity through engaging discussions with participants from diverse backgrounds and perspectives. To this end, we propose LLM Discussion, a three-phase discussion framework that facilitates vigorous and diverging idea exchanges and ensures convergence to creative answers. Moreover, we adopt a role-playing technique by assigning distinct roles to LLMs to combat the homogeneity of LLMs. We evaluate the efficacy of the proposed framework with the Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test through both LLM evaluation and human study. The results show that our proposed framework outperforms single-LLM approaches and existing multi-LLM frameworks across various creativity metrics. The code is available at https://github.com/lawraa/LLM-Discussion.
Related papers
- Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
We introduce AlphaLLM for the self-improvements of Large Language Models.
It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop.
Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
arXiv Detail & Related papers (2024-04-18T15:21:34Z) - Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the
Key? [84.36332588191623]
We propose a novel group discussion framework to enrich the set of discussion mechanisms.
We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt.
arXiv Detail & Related papers (2024-02-28T12:04:05Z) - Assessing and Understanding Creativity in Large Language Models [33.37237667182931]
This paper aims to establish an efficient framework for assessing the level of creativity in large language models (LLMs)
By adapting the Torrance Tests of Creative Thinking, the research evaluates the creative performance of various LLMs across 7 tasks.
We found that the creativity of LLMs primarily falls short in originality, while excelling in elaboration.
arXiv Detail & Related papers (2024-01-23T05:19:47Z) - Boosting Large Language Model for Speech Synthesis: An Empirical Study [86.89548753080432]
Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision.
We conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E.
We compare three integration methods between LLMs and speech models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder
arXiv Detail & Related papers (2023-12-30T14:20:04Z) - LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language
Models [56.25156596019168]
This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs)
Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
arXiv Detail & Related papers (2023-11-30T03:59:31Z) - CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine
Chain-of-Thought Prompting for Multi-domain NLU Tasks [46.862929778121675]
Chain-of-Thought prompting is popular in reasoning tasks, but its application to Natural Language Understanding (NLU) is under-explored.
Motivated by multi-step reasoning of Large Language Models (LLMs), we propose Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach.
arXiv Detail & Related papers (2023-10-23T06:54:51Z) - Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents.
Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes.
We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
arXiv Detail & Related papers (2023-09-30T07:11:39Z) - User-Controlled Knowledge Fusion in Large Language Models: Balancing
Creativity and Hallucination [5.046007553593371]
Large Language Models (LLMs) generate diverse, relevant, and creative responses.
Striking a balance between the LLM's imaginative capabilities and its adherence to factual information is a key challenge.
This paper presents an innovative user-controllable mechanism that modulates the balance between an LLM's imaginative capabilities and its adherence to factual information.
arXiv Detail & Related papers (2023-07-30T06:06:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.