eprintid: 10194858
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/48/58
datestamp: 2024-07-19 13:34:12
lastmod: 2024-07-19 13:34:12
status_changed: 2024-07-19 13:34:12
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Fang, M
creators_name: Deng, S
creators_name: Zhang, Y
creators_name: Shi, Z
creators_name: Chen, L
creators_name: Pechenizkiy, M
creators_name: Wang, J
title: Large Language Models Are Neurosymbolic Reasoners
ispublished: pub
divisions: UCL
divisions: B04
divisions: F48
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initializing the LLM agent and informing it of its role. The agent then receives observations and a set of valid actions from the text-based games, along with a specific symbolic module. With these inputs, the LLM agent chooses an action and interacts with the game environments. Our experimental results demonstrate that our method significantly enhances the capability of LLMs as automated agents for symbolic reasoning, and our LLM agent is effective in text-based games involving symbolic tasks, achieving an average performance of 88% across all tasks.
date: 2024-03-25
date_type: published
publisher: Association for the Advancement of Artificial Intelligence (AAAI)
official_url: http://dx.doi.org/10.1609/aaai.v38i16.29754
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2268264
doi: 10.1609/aaai.v38i16.29754
lyricists_name: Wang, Jun
lyricists_id: JWANG00
actors_name: Wang, Jun
actors_id: JWANG00
actors_role: owner
full_text_status: public
pres_type: paper
publication: Proceedings of the AAAI Conference on Artificial Intelligence
volume: 38
number: 16
pagerange: 17985-17993
event_title: The 38th Annual AAAI Conference on Artificial Intelligence
issn: 2159-5399
book_title: Proceedings of the AAAI Conference on Artificial Intelligence
citation:        Fang, M;    Deng, S;    Zhang, Y;    Shi, Z;    Chen, L;    Pechenizkiy, M;    Wang, J;      (2024)    Large Language Models Are Neurosymbolic Reasoners.                     In:  Proceedings of the AAAI Conference on Artificial Intelligence.  (pp. pp. 17985-17993).  Association for the Advancement of Artificial Intelligence (AAAI)       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10194858/1/2401.09334v1.pdf