From Human-to-Human to Human-to-Bot Conversations in Software Engineering
- URL: http://arxiv.org/abs/2405.12712v1
- Date: Tue, 21 May 2024 12:04:55 GMT
- Title: From Human-to-Human to Human-to-Bot Conversations in Software Engineering
- Authors: Ranim Khojah, Francisco Gomes de Oliveira Neto, Philipp Leitner,
- Abstract summary: We aim to understand the dynamics of conversations that occur during modern software development after the integration of AI and chatbots.
We compile existing conversation attributes with humans and NLU-based chatbots and adapt them to the context of software development.
We present similarities and differences between human-to-human and human-to-bot conversations.
We conclude that the recent conversation styles that we observe with LLM-chatbots can not replace conversations with humans.
- Score: 3.1747517745997014
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software developers use natural language to interact not only with other humans, but increasingly also with chatbots. These interactions have different properties and flow differently based on what goal the developer wants to achieve and who they interact with. In this paper, we aim to understand the dynamics of conversations that occur during modern software development after the integration of AI and chatbots, enabling a deeper recognition of the advantages and disadvantages of including chatbot interactions in addition to human conversations in collaborative work. We compile existing conversation attributes with humans and NLU-based chatbots and adapt them to the context of software development. Then, we extend the comparison to include LLM-powered chatbots based on an observational study. We present similarities and differences between human-to-human and human-to-bot conversations, also distinguishing between NLU- and LLM-based chatbots. Furthermore, we discuss how understanding the differences among the conversation styles guides the developer on how to shape their expectations from a conversation and consequently support the communication within a software team. We conclude that the recent conversation styles that we observe with LLM-chatbots can not replace conversations with humans due to certain attributes regarding social aspects despite their ability to support productivity and decrease the developers' mental load.
Related papers
- Empirical Study of Symmetrical Reasoning in Conversational Chatbots [0.0]
This work explores the capability of conversational chatbots powered by large language models (LLMs) to understand predicate symmetry.
We assess the symmetrical reasoning of five chatbots: ChatGPT 4, Huggingface chat AI, Microsoft's Copilot AI, LLaMA through Perplexity, and Gemini Advanced.
Experiment results reveal varied performance among chatbots, with some approaching human-like reasoning capabilities.
arXiv Detail & Related papers (2024-07-08T08:38:43Z) - LLM Roleplay: Simulating Human-Chatbot Interaction [52.03241266241294]
We propose a goal-oriented, persona-based method to automatically generate diverse multi-turn dialogues simulating human-chatbot interaction.
Our method can simulate human-chatbot dialogues with a high indistinguishability rate.
arXiv Detail & Related papers (2024-07-04T14:49:46Z) - Supporting Student Decisions on Learning Recommendations: An LLM-Based
Chatbot with Knowledge Graph Contextualization for Conversational
Explainability and Mentoring [0.0]
We propose an approach to utilize chatbots as mediators of the conversation and sources of limited and controlled generation of explanations.
A group chat approach is developed to connect students with human mentors, either on demand or in cases that exceed the chatbots's pre-defined tasks.
arXiv Detail & Related papers (2024-01-16T17:31:35Z) - Interactive Conversational Head Generation [68.76774230274076]
We introduce a new conversation head generation benchmark for synthesizing behaviors of a single interlocutor in a face-to-face conversation.
The capability to automatically synthesize interlocutors which can participate in long and multi-turn conversations is vital and offer benefits for various applications.
arXiv Detail & Related papers (2023-07-05T08:06:26Z) - PLACES: Prompting Language Models for Social Conversation Synthesis [103.94325597273316]
We use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting.
We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations.
arXiv Detail & Related papers (2023-02-07T05:48:16Z) - A Deep Learning Approach to Integrate Human-Level Understanding in a
Chatbot [0.4632366780742501]
Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second.
We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence.
arXiv Detail & Related papers (2021-12-31T22:26:41Z) - CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement
Learning [60.348822346249854]
This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns.
We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning.
To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
arXiv Detail & Related papers (2021-10-08T07:44:47Z) - Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn
Chatbot Responding with Intention [55.77218465471519]
This paper proposes an innovative framework to train chatbots to possess human-like intentions.
Our framework included a guiding robot and an interlocutor model that plays the role of humans.
We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
arXiv Detail & Related papers (2021-03-30T15:24:37Z) - Spot The Bot: A Robust and Efficient Framework for the Evaluation of
Conversational Dialogue Systems [21.36935947626793]
emphSpot The Bot replaces human-bot conversations with conversations between bots.
Human judges only annotate for each entity in a conversation whether they think it is human or not.
emphSurvival Analysis measures which bot can uphold human-like behavior the longest.
arXiv Detail & Related papers (2020-10-05T16:37:52Z) - Joint Mind Modeling for Explanation Generation in Complex Human-Robot
Collaborative Tasks [83.37025218216888]
We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations.
The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications.
Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
arXiv Detail & Related papers (2020-07-24T23:35:03Z)
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.