TPE: Towards Better Compositional Reasoning over Conceptual Tools with
Multi-persona Collaboration
- URL: http://arxiv.org/abs/2309.16090v1
- Date: Thu, 28 Sep 2023 01:18:53 GMT
- Title: TPE: Towards Better Compositional Reasoning over Conceptual Tools with
Multi-persona Collaboration
- Authors: Hongru Wang, Huimin Wang, Lingzhi Wang, Minda Hu, Rui Wang, Boyang
Xue, Hongyuan Lu, Fei Mi, Kam-Fai Wong
- Abstract summary: Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools.
We introduce a multi-persona collaboration framework: Think-Plan-Execute (TPE)
This framework decouples the response generation process into three distinct roles: Thinker, Planner, and Executor.
- Score: 38.63262397010507
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Large language models (LLMs) have demonstrated exceptional performance in
planning the use of various functional tools, such as calculators and
retrievers, particularly in question-answering tasks. In this paper, we expand
the definition of these tools, centering on conceptual tools within the context
of dialogue systems. A conceptual tool specifies a cognitive concept that aids
systematic or investigative thought. These conceptual tools play important
roles in practice, such as multiple psychological or tutoring strategies being
dynamically applied in a single turn to compose helpful responses. To further
enhance the reasoning and planning capability of LLMs with these conceptual
tools, we introduce a multi-persona collaboration framework: Think-Plan-Execute
(TPE). This framework decouples the response generation process into three
distinct roles: Thinker, Planner, and Executor. Specifically, the Thinker
analyzes the internal status exhibited in the dialogue context, such as user
emotions and preferences, to formulate a global guideline. The Planner then
generates executable plans to call different conceptual tools (e.g., sources or
strategies), while the Executor compiles all intermediate results into a
coherent response. This structured approach not only enhances the
explainability and controllability of responses but also reduces token
redundancy. We demonstrate the effectiveness of TPE across various dialogue
response generation tasks, including multi-source (FoCus) and multi-strategy
interactions (CIMA and PsyQA). This reveals its potential to handle real-world
dialogue interactions that require more complicated tool learning beyond just
functional tools. The full code and data will be released for reproduction.
Related papers
- COLT: Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT.
COLT captures semantic similarities between user queries and tool descriptions.
It also takes into account the collaborative information of tools.
arXiv Detail & Related papers (2024-05-25T06:41:23Z) - Planning and Editing What You Retrieve for Enhanced Tool Learning [31.963485987789852]
This paper introduces a novel PLUTO (Planning, Learning, and Understanding for TOols) approach, encompassing Plan-and-Retrieve (P&R) and Edit-and-Ground (E&G) paradigms.
Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.
arXiv Detail & Related papers (2024-03-30T18:41:51Z) - Self-Explanation Prompting Improves Dialogue Understanding in Large
Language Models [52.24756457516834]
We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs)
This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks.
Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
arXiv Detail & Related papers (2023-09-22T15:41:34Z) - Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs)
The system is into three inter-connected core processes-interaction, reflection, and reaction.
Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
arXiv Detail & Related papers (2023-09-15T02:42:03Z) - AssistGPT: A General Multi-modal Assistant that can Plan, Execute,
Inspect, and Learn [25.510696745075688]
We propose a multi-modal AI assistant, AssistGPT, with an interleaved code and language reasoning approach called Plan, Execute, Inspect, and Learn.
The Planner is capable of using natural language to plan which tool in Executor should do next based on the current reasoning progress.
We conducted experiments on A-OKVQA and NExT-QA benchmarks, achieving state-of-the-art results.
arXiv Detail & Related papers (2023-06-14T17:12:56Z) - SOCIOFILLMORE: A Tool for Discovering Perspectives [10.189255026322996]
SOCIOFILLMORE is a tool which helps to bring to the fore the perspective that a text expresses in depicting an event.
Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics.
arXiv Detail & Related papers (2022-03-07T14:42:22Z) - Learning an Effective Context-Response Matching Model with
Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination.
We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner.
Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
arXiv Detail & Related papers (2020-09-14T08:44:46Z) - Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue
Representation Learning [50.5572111079898]
Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc.
While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive.
In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks.
arXiv Detail & Related papers (2020-02-27T04:36:52Z)
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.