# Reliable Conversational Agents under ASP Control that Understand Natural Language

arXiv: https://arxiv.org/abs/2502.09237 · HTML: https://mlsanity.com/paper/2502.09237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models](https://mlsanity.com/paper/2406.02061/) (2024)
- [Reinforcement Learning for Optimizing RAG for Domain Chatbots](https://mlsanity.com/paper/2401.06800/) (2024)
- [A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns](https://mlsanity.com/paper/1610.08115/) (2016)
- [Reliable Natural Language Understanding with Large Language Models and Answer Set Programming](https://mlsanity.com/paper/2302.03780/) (2023)
- [Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems](https://mlsanity.com/paper/2403.11413/) (2024)
- [CASPR: A Commonsense Reasoning-based Conversational Socialbot](https://mlsanity.com/paper/2110.05387/) (2021)
- [Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot](https://mlsanity.com/paper/2401.07216/) (2024)

## Abstract (arXiv)

Efforts have been made to make machines converse like humans in the past few decades. The recent techniques of Large Language Models (LLMs) make it possible to have human-like conversations with machines, but LLM's flaws of lacking understanding and reliability are well documented. We believe that the best way to eliminate this problem is to use LLMs only as parsers to translate text to knowledge and vice versa and carry out the conversation by reasoning over this knowledge using the answer set programming. I have been developing a framework based on LLMs and ASP to realize reliable chatbots that "understand" human conversation. This framework has been used to develop task-specific chatbots as well as socialbots. My future research is focused on making these chatbots scalable and trainable.