A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916573432774656 |
|---|---|
| author | Zeng, Yankai Rajashekharan, Abhiramon Basu, Kinjal Wang, Huaduo Arias, Joaquín Gupta, Gopal |
| author_facet | Zeng, Yankai Rajashekharan, Abhiramon Basu, Kinjal Wang, Huaduo Arias, Joaquín Gupta, Gopal |
| contents | The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on Answer Set Programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation, which it dynamically regulates to achieve its specific purpose, and (iii) no deviation from the main topic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18498 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP Zeng, Yankai Rajashekharan, Abhiramon Basu, Kinjal Wang, Huaduo Arias, Joaquín Gupta, Gopal Computation and Language Artificial Intelligence Logic in Computer Science The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on Answer Set Programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation, which it dynamically regulates to achieve its specific purpose, and (iii) no deviation from the main topic. |
| title | A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP |
| topic | Computation and Language Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2407.18498 |