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REVIEW 3 major objections 5 minor 1 cited by

Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that text-based human-LLM chat need not follow strict turn-taking: a chatbot that overlaps with the user's typing by preemptive answering and backchanneling was perceived as more communicative, immersive, faster, and more…

desk verdict A credible design exploration of text-based overlap in human-LLM chat, but the main user study is confounded: the overlap interface and the finetuned model change together, so the perceived benefits can't be cleanly pinned on overlap itself. read the letter →

arxiv 2501.18103 v1 pith:QQRG4AIX submitted 2025-01-30 cs.HC cs.CL

JiWoo Kim , Minsuk Chang , JinYeong Bak This is my paper · ORCID
classification cs.HCcs.CL
keywords human-AIinteractiontext-basedchatbotoverlappingmessagesturn-takinglargelanguagemodelbackchannelingreal-timetypinginterruption
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that strict turn-taking is an unnecessary constraint in text-based human-LLM conversations. Drawing on a formative study in which pairs of people chatted with visible real-time typing, it identifies three overlap behaviors—preemptive answers, backchanneling, and deletion—and builds OverlapBot, an LLM chatbot that performs the first two and reacts to interruptions by deleting and regenerating. In a within-subject study with 18 users, OverlapBot was perceived as more communicative and immersive than a conventional turn-taking chatbot, with faster exchanges and shorter messages. If correct, this opens a design space in which text chat with LLMs mirrors the fluidity of spoken conversation rather than a chess-like alternation of turns.

What carries the argument

The central object is OverlapBot, a prototype web chatbot built on Llama3-8B finetuned with customized datasets. The mechanism has three parts: real-time typing display (every keystroke visible, chatbot output streamed character by character); a finetuned model that decides at each point whether to [Await] or [Overlap], and if overlapping whether to produce [Understanding] (backchannel) or [Answer] (preemptive response); and interruption handling, where the user's overlap causes the chatbot to delete its prior text and regenerate, with a 130-character threshold that leaves '...' to signal continuation. This machinery is what translates observed human overlap behaviors into an LLM policy.

What would settle it

Run the same 18-participant design with two conditions that differ only in whether typing is visible and overlap is allowed, holding the model, response style, and response length identical; if users do not rate the overlap condition as more communicative and immersive, the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that text-based overlap is not only possible in human-LLM interaction but is instinctively adopted and positively valued. OverlapBot lets both parties type simultaneously: the user sees the chatbot's words appear in real time and can interrupt it, and the chatbot backchannels ('yeah') or gives preemptive answers before the user finishes a sentence, deleting its own unfinished response when interrupted. In the user study, participants produced more turns per minute, sent shorter messages, and described the experience as like talking to a real person; they read preemptive answers as evidence of listening, acknowledged backchanneling without replying, and used brief commands like 'stop' or 'okay' to cut the chatbot off. The paper concludes that overlap-capable interfaces make text-based human-LLM conversation more natural and efficient than strict turn-taking.

Load-bearing premise

The load-bearing premise is that the perceived benefits come from the overlapping capability itself and not from other differences between the two chatbots, since OverlapBot was finetuned and produced shorter responses than the unfinetuned baseline it was compared against.

Editorial extensions

If this is right

  • Text-based LLM interfaces can be built so that typing visibility alone creates overlap opportunities, and users will use them without explicit instruction.
  • Conversations with overlap-capable chatbots move faster: the study measured higher turns per minute and shorter messages for both the user and the chatbot.
  • Users interpret a chatbot's mid-typing responses as active listening, which drives the perceived humanness and immersion.
  • Interruptions can replace stop buttons: users naturally type short commands to halt the chatbot, and the chatbot can regenerate based on the interruption.
  • Overlap designs need user control, since some users find frequent or poorly timed overlaps intrusive; adjustable frequency and typing visibility are implied design requirements.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If overlap reduces the need for complete prompts, prompt engineering may become less central: users can start half-formed questions and let the chatbot's early responses guide them, a shift the paper hints at but does not test directly.
  • The same overlap mechanisms could be transplanted to voice agents or multimodal interfaces, where timing and interruption cues already exist; the paper's deletion-based resolution suggests text offers a unique repair channel that speech lacks.
  • A testable extension would measure whether overlap benefits persist in task-oriented settings like summarization or time-critical information seeking, where the paper's design discussion predicts moderate benefits at best.
  • Overlap could change trust dynamics: if users read preemptive answers as listening, then confidently wrong early guesses might be treated as more credible than they should be—a risk the paper does not address.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes replacing strict turn-taking in text-based human-LLM chat with an interface that supports simultaneous typing and overlapping messages. Study 1 (a formative probe with seven dyads) identifies preemptive answering, backchanneling, and deletion as natural overlap behaviors. The authors then build OverlapBot, a Llama3-8B model fine-tuned with Switchboard and instruction-tuning data to reproduce these behaviors. Study 2 (a within-subject user study with 18 participants) compares OverlapBot to a conventional turn-taking chatbot and reports that OverlapBot produced shorter messages, more turns, and qualitative perceptions of being more communicative, immersive, and fast. The paper concludes that overlap capability fosters faster and more natural interactions and offers design insights for overlap-capable text-based AI systems.

Significance. If the causal attribution is accepted, the paper opens a useful design space for text-based human-LLM interaction, extending prior real-time human-human messaging results to LLM agents. The work has genuine strengths: the formative study is well motivated, the prototype is concretely described, the fine-tuning strategy uses public datasets and is evaluated automatically in Appendix A, and the user study includes behavioral logs and rich interview excerpts. However, the central claim is currently underdetermined because the Study 2 comparison varies both the interface and the underlying model, and because the quantitative evidence lacks inferential statistics. These issues directly affect the abstract's claim that overlap itself fosters faster and more natural interactions.

major comments (3)
  1. [Section 5, first two paragraphs; Appendix A] The central comparison is confounded: OverlapBot runs a Llama3-8B fine-tuned on Switchboard and instruction data, while the 'conventional chat system' uses 'the basic, unfinetuned Llama3-8B model.' The OverlapBot condition also adds real-time typing display, backchanneling, preemptive answers, and deletion. Consequently, the perceived differences in 'communicative' and 'immersive' interactions reported in Section 5.1.1 cannot be uniquely attributed to the overlapping capability; they may reflect the fine-tuned model's more conversational tone, its shorter responses, or the real-time streaming display. The abstract and Section 7 state the conclusion as if overlap is the active ingredient. Please add a condition that holds the model and response style fixed while toggling overlap, or substantially reframe the contribution as an evaluation of the OverlapBot system as a whole rather than a demonstration that overlap per se drives the effect.
  2. [Section 5.1.1, 'Brief Response'; Table 1] The paper's own 'Brief Response' theme undercuts the speed attribution. Table 1 shows OverlapBot produces notably shorter chatbot messages (133.40 vs. 177.64 characters) and shorter user messages (43.18 vs. 62.36), and participants explicitly praised speed while also criticizing brevity and lack of detail. The text says the brevity 'was likely influenced by a conversation dataset ... not a problem of overlapping itself,' but no control condition separates response brevity from overlap. A parsimonious alternative explanation is that the 'speedy' perception is driven by the fine-tuned model's concise, chat-style outputs rather than by overlap. At minimum, the analysis should examine whether perceived speed tracks the measured overlap rate or message length, and the discussion should acknowledge that the interface and the response style are not separated.
  3. [Table 1] The quantitative comparison in Table 1 reports only means and standard deviations for each condition, with no paired significance tests, confidence intervals, or effect sizes, despite the within-subject design. Statements such as OverlapBot 'facilitated shorter message lengths and a higher number of turns' and that the chatbot sent messages more frequently, 'indicating its ability to provide more information within the same timeframe,' are therefore descriptive rather than statistically supported. Please add appropriate paired inferential analyses (e.g., paired t-tests or Wilcoxon signed-rank tests with effect sizes), or explicitly restrict the quantitative section to descriptive observations and avoid drawing causal conclusions from it.
minor comments (5)
  1. [Section 5] The paper states that participants were asked which interface they preferred, but it does not report the number who preferred each condition or quote a preference statistic; adding this information would strengthen the qualitative preference claims.
  2. [Section 5.1] Thematic analysis is described as being conducted by three authors, but the paper does not report a codebook, coding procedure, or inter-rater agreement; a brief description and per-theme frequencies would aid reproducibility and interpretation.
  3. [Section 4.3] The 130-character response truncation threshold is justified only by 'empirical testing'; please report the test results or describe the threshold as a preliminary design parameter requiring further validation.
  4. [References] Reference [53] duplicates reference [52] (both are Skantze 2021, 'Turn-taking in Conversational Systems and Human-Robot Interaction'); the duplicate should be removed and subsequent numbering corrected.
  5. [Appendix A] The fine-tuning and evaluation section does not report decoding parameters such as temperature, max tokens, or the exact prompts used for generation; including these would improve reproducibility of the automatic evaluation in Table 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claim is an external user-study evaluation, not derived from fitted quantities or self-citations.

full rationale

The paper's central claim—that OverlapBot was perceived as more communicative and immersive than a traditional turn-taking chatbot—rests on a within-subject user study with 18 participants who interacted with both systems and provided qualitative and quantitative feedback. This is an external, human-judgment evaluation of a system the authors built, not a derivation from the training data or from a fitted parameter. The finetuned model in Appendix A is evaluated against GPT baselines on classification and generation metrics, but those results are presented as a capability check, not as the evidence for the user-perception claim. The paper does not define overlap in terms of perceived communicative value, nor does it fit a parameter from Study 2 and then predict the same judgment. The acknowledged confound—that Study 2 compared a finetuned OverlapBot against an unfinetuned Llama3-8B baseline, so the interface and the model both varied—is a threat to internal validity, not circularity. The paper explicitly notes the brevity difference and attributes it to the conversation dataset rather than overlap itself, which is a limitation statement rather than a circular move. No load-bearing self-citations are used; the authors are not relying on their own prior uniqueness theorems or ansatz smuggled in via citation. Accordingly, no circular step can be exhibited, and the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claims rest on transfer assumptions from human conversation research, on the validity of self-report, and on a confounded baseline comparison. The only hand-tuned numeric design parameter visible is the 130-character truncation threshold.

free parameters (1)
  • Response truncation threshold = 130 characters
    Chosen via empirical testing in Section 4.3 ('Through empirical testing, we found that 130 characters maintain the optimal conversation flow'); it affects when interrupted chatbot responses are truncated and regenerated, but no derivation is provided.
assumptions (4)
  • domain assumption Overlapping behaviors seen in human-human text conversations (preemptive response, backchanneling, deletion) transfer to human-LLM interactions and are perceived positively.
    Underlies the formative study and the design of OverlapBot; Sections 3 and 4 assume these behaviors are desirable for an AI partner.
  • domain assumption Self-reported impressions (communicative, immersive, natural) are treated as valid evidence of interaction quality.
    User study conclusions rely on thematic analysis of open-ended survey and interview responses (Section 5).
  • domain assumption The baseline conventional chatbot (unfinetuned Llama3-8B) is an appropriate control for isolating the effect of overlap.
    Section 5 compares OverlapBot to this baseline, but the baseline differs in model finetuning and response length; the assumption is needed to attribute observed differences to overlap.
  • domain assumption Switchboard dialogue act labels can be consolidated and used to supervise overlap timing and dialogue act classification.
    Appendix A.3 maps 43 SWDA dialogue acts to [Understanding]/[Answer] tags and [Overlap]/[Await] labels; this mapping assumes the corpus annotations are a valid training signal for overlapping behavior.

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Cite this review

Pith. "Pith review of Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions." pith.science (2026). https://pith.science/paper/QQRG4AIX

@misc{pith2026250118103,
  author       = {Pith},
  title        = {Pith review of: Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QQRG4AIX}},
  note         = {Machine review of arXiv:2501.18103}
}
read the original abstract

Traditional text-based human-AI interactions often adhere to a strict turn-taking approach. In this research, we propose a novel approach that incorporates overlapping messages, mirroring natural human conversations. Through a formative study, we observed that even in text-based contexts, users instinctively engage in overlapping behaviors like "A: Today I went to-" "B: yeah." To capitalize on these insights, we developed OverlapBot, a prototype chatbot where both AI and users can initiate overlapping. Our user study revealed that OverlapBot was perceived as more communicative and immersive than traditional turn-taking chatbot, fostering faster and more natural interactions. Our findings contribute to the understanding of design space for overlapping interactions. We also provide recommendations for implementing overlap-capable AI interactions to enhance the fluidity and engagement of text-based conversations.

Figures

Figures reproduced from arXiv: 2501.18103 by the authors.

Figure 1
Figure 1. Introduction of overlapping into text-based chat interaction with AI: The chatbot generates overlapping messages [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Current turn-taking chat with an LLM. Motivated by the absence of overlap points, we identified a new opportunity to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visually changed UI from typing status to sent status. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Examples of the types of overlap by OverlapBot. While the user is typing, OverlapBot can provide listener cues [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: The chatbot regenerates an answer based on the interruption, presented in a timely order from (a) to (c): Before the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: First new human-LLM interaction: The user checks OverlapBot’s active listening through its preemptive answering. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Second new human-LLM interaction: The user makes no verbal reaction to OverlapBot’s backchanneling but acknowl [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Third new human-LLM interaction: The user makes short interruption commands to OverlapBot. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: An example of a customized dataset engineered for modeling overlapping behaviors in human-AI interactions. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.