# Cognitively Inspired Components for Social Conversational Agents

arXiv: https://arxiv.org/abs/2311.05450 · HTML: https://mlsanity.com/paper/2311.05450/

## Key facts

- Cited by 1 papers in the map (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 22 of its 97 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## 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, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback](https://mlsanity.com/paper/2208.03270/) (2022)
- [Generating Empathetic Responses by Looking Ahead the User's Sentiment](https://mlsanity.com/paper/1906.08487/) (2019)
- [A Discrete CVAE for Response Generation on Short-Text Conversation](https://mlsanity.com/paper/1911.09845/) (2019)
- [Deep Retrieval-Based Dialogue Systems: A Short Review](https://mlsanity.com/paper/1907.12878/) (2019)
- [Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents](https://mlsanity.com/paper/2108.02174/) (2021)
- [Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory](https://mlsanity.com/paper/2003.03645/) (2020)
- [A comprehensive solution to retrieval-based chatbot construction](https://mlsanity.com/paper/2106.06139/) (2021)
- [Causal Learning by a Robot with Semantic-Episodic Memory in an Aesop's Fable Experiment](https://mlsanity.com/paper/2003.00274/) (2020)
- [A Proposal for Intelligent Agents with Episodic Memory](https://mlsanity.com/paper/2005.03182/) (2020)
- [Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity](https://mlsanity.com/paper/2204.05138/) (2022)

## Built on by (most cited first, 1 of 1)

- [What Do You Mean? Exploring How Humans and AI Interact with Symbols and Meanings in Their Interactions](https://mlsanity.com/paper/2510.05378/) (2025)

## Abstract (arXiv)

Current conversational agents (CA) have seen improvement in conversational quality in recent years due to the influence of large language models (LLMs) like GPT3. However, two key categories of problem remain. Firstly there are the unique technical problems resulting from the approach taken in creating the CA, such as scope with retrieval agents and the often nonsensical answers of former generative agents. Secondly, humans perceive CAs as social actors, and as a result expect the CA to adhere to social convention. Failure on the part of the CA in this respect can lead to a poor interaction and even the perception of threat by the user. As such, this paper presents a survey highlighting a potential solution to both categories of problem through the introduction of cognitively inspired additions to the CA. Through computational facsimiles of semantic and episodic memory, emotion, working memory, and the ability to learn, it is possible to address both the technical and social problems encountered by CAs.