Text Overlap: An LLM with Human-like Conversational Behaviors (2025)

Abstract: Traditional text-based human-AI interactions typically follow a strict turn-taking approach. This rigid structure limits conversational flow, unlike natural human conversations, which can freely incorporate overlapping speech. However, our pilot study suggests that even in text-based interfaces, overlapping behaviors such as backchanneling and proactive responses lead to more natural and functional exchanges. Motivated by these findings, we introduce text-based overlapping interactions as a new challenge in human-AI communication, characterized by real-time typing, diverse response types, and interruptions. To enable AI systems to handle such interactions, we define three core tasks: deciding when to overlap, selecting the response type, and generating utterances. We construct a synthetic dataset for these tasks and train OverlapBot, an LLM-driven chatbot designed to engage in text-based overlapping interactions. Quantitative and qualitative evaluations show that OverlapBot increases turn exchanges compared to traditional turn-taking systems, with users making 72% more turns and the chatbot 130% more turns, which is perceived as efficient by end-users. This finding supports overlapping interactions and enhances communicative efficiency and engagement.

## introduction human-to-human conversations differ from chess, where turns are strictly alternated. in human-tohuman conversation, overlaps and interruptions are common, requiring participants to coordinate who speaks, when to stop, and when to continue @xcite . on the other hand, current text-based chat interactions follow strict turn-taking, similar to playing chess. this applies not only to human-human interactions but also to interactions with large language models (llm), where users must wait for the chatbot to respond refining strict turn-taking remains relatively underexplored in nlp, despite efforts in speech and robotics to improve turn-taking dynamics @xcite @xcite @xcite . speech-based systems have primarily focused on reducing awkward silences @xcite @xcite , while robotic systems have shown that improved turn-taking enhances conversational naturalness @xcite @xcite . although a recent text-based study @xcite introduced duplex response generation, existing studies have yet to refine turn-taking based on specific conversational behaviors observed in human dialogue. strict turn-taking may overlook important conversational features, both in terms of naturalness and functionality. to investigate this, we conducted a pilot study where seven pairs of participants engaged in text-based conversations using a real-time chat interface that allowed simultaneous typing and message visibility. our observations revealed that overlapping interactions enabled functional conversational behaviors, particularly backchanneling, where participants provided brief acknowledgments (e.g., yeah or got it) while reading the other person's message, and preemptive answering, where they anticipated and responded before a question or statement was fully articulated. these findings suggest that allowing overlap in text-based interactions fosters more natural and functional exchanges. motivated by this finding, we introduce text overlapping interactions as a new challenge in human-ai communication, where interactions involve realtime typing, diverse response types, and interruptions. unlike strict turn-taking systems, ai capable of handling overlap must dynamically manage when to interject, provide backchannels, or generate preemptive responses. to address this, we define three core evaluation tasks: (1) timing classification, deciding whether to overlap or wait; (2) action classification, determining the appropriate response type, such as backchanneling or producing a full response; and (3) utterance generation, producing natural overlapping responses that maintain conversational flow. by tackling these challenges, we aim to develop ai systems that better align with human conversational behaviors. thus, we develop overlapbot, an llm-driven chatbot, using a synthetic dataset constructed from a conversation dataset @xcite and an instruction-tuning dataset @xcite . over-lapbot is finetuned on llama3-8b with parameterefficient tuning, optimizing it for the timing classification (when to overlap), action classification (response type selection), and utterance generation (producing overlapping responses). we develop a dedicated chat interface that supports overlapping functionalities such as real-time typing and interruptions. our evaluation shows that overlapbot improves both system performance and the end-user experience in overlapping interactions. it demonstrated better performance than the baselines in timing accuracy, act classification, and utterance generation, while a user study with 18 participants found it more communicative and immersive than a conventional turn-taking chatbot. overlapbot generates more concise messages and increases and enables faster turn-taking, highlighting the benefits of overlapping interactions for efficiency and engagement. in summary, our contributions include: ## text overlap interactions we characterize text-based overlapping interactions based on key findings from our pilot study (appendix b). in this study, 14 participants engaged in 10-minute real-time chat conversations on decisionmaking tasks. the interface allowed them to see their partner's typing as it happened, creating a conversational flow similar to spoken dialogue. from our observations, we identify three key elements that characterize text-overlapping interactions: real-time typing the interface displays participants' typing activities in real-time, allowing both parties to see input as it is being written. this shared visibility creates opportunities for overlap by enabling responses before message completion. for instance, if a user types i want to be, and their conversation partner simultaneously responds with yeah, the overlap occurs at the word be in the user's utterance. types of response text-based overlap manifests in two primary forms: backchanneling and preemptive answering ( interruptions and deletions speakers often adjust their responses when an overlap occurs, either deleting unfinished text or rephrasing to maintain conversational coherence. for example, if a user begins typing i was thinking we could try -, but the other person interrupts with let's go to the italian place!, the user deletes their unfinished sentence dialogues timing action utterance user: have ground truth: [await] hypothesis: [await] ✓ --hypothesis: [overlap] [answer] i tried, but i couldn't. ✗ ✗ ✗ user: have you painted ground truth: [overlap] [understanding] mm-hmm. hypothesis: [overlap] [answer] i painted yesterday. ✓ ✗ ✗ user: have you painted anything recently? ground truth: [overlap] [answer] yes, i painted a small landscape last weekend. hypothesis: [overlap] [answer] mm-hmm. ✓ ✓ ✗ and instead replies with yeah, that works!, adjusting their response to fit the new conversational direction. ## approach 3.1 training strategy to enable llms to handle text overlap interactions, we establish three core evaluation tasks as shown in we created a synthetic dataset by modifying existing datasets to align with the three core tasks. the final dataset consists of 15,377 training samples, 6,482 validation samples, and 6,978 test samples. an example of the modified format is shown below. instruct evaluate whether the interlocutor would overlap this utterance or wait his turn to come. if your evaluation is to overlap, return your evaluation as [overlap] _dialogue_act_ _answer_. you have to choose a _dialogue_act_: either [understanding] or [answer] . you have to fill _answer_ with your own answer to this utterance. otherwise, if your evaluation is to wait, return your evaluation as only [await] . user have you painted assistant [overlap] [understanding] mm-hmm. we created the synthetic dataset from two existing datasets: a conversation dataset and an instruction-tuning dataset. the first dataset, the switchboard dialogue act corpus (swda), consists of 1,155 five-minute telephone conversations between 440 participants discussing various topics such as child care, recycling, and news media @xcite . we selected swda for its detailed dialogue annotations, which include task description timing decide whether to overlap or wait. example user typing "have you painted," then model predicts [overlap] or [await] . action choose the type of response when overlapping. example if [understanding] , model selects backchanneling. if [answer] , model selects full answer. utt. generate a natural response based on the action selection. example if [understanding] , model generates umhmm. if [answer] , model generates i painted something (...) we finetuned the llama3 8b instruct model using parameter-efficient techniques (ai@meta, 2024; @xcite . training details are provided in appendix d. we evaluated the chatbots' automatic performance on classification accuracy (f1 score) and reference-based generation accuracy (bleu @xcite , rouge-l @xcite ). for our baseline models, we used llama3 ## conclusion and future work we introduce text-based overlapping features into human-ai interactions. we show the key characteristics of text overlapping and develop specific tasks for llms to handle such interactions. our implementation with a finetuned llm shows improvements in interaction efficiency and naturalness compared to traditional turn-taking systems. our results highlight key directions for extending this work. while our implementation shows the potential of text-based overlapping, further research is needed to assess its effectiveness across different interaction scenarios. additionally, developing metrics to balance interaction speed and response quality is meaningful for real-world applications. furthermore, extending this work to multimodal interactions that integrate text and speech can be a meaningful direction @xcite . understanding how llms process these overlaps could lead to more responsive ai systems across modalities.