Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models
Pith reviewed 2026-05-22 20:55 UTC · model grok-4.3
The pith
A task-oriented taxonomy structures the review of multi-turn LLM interactions by separating instruction following from conversational engagement.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
This survey provides a comprehensive review of recent progress in evaluating and enhancing multi-turn LLM interactions, centered on a task-oriented taxonomy spanning instruction following in domains such as mathematics and coding, and conversational engagement in role-playing, healthcare, education, and adversarial jailbreak settings. It systematically examines the challenges of maintaining context, coherence, fairness, and responsiveness across prolonged dialogues, organizes benchmarks and datasets into coherent categories, and reviews enhancement methodologies including model-centric strategies, external integration approaches, and agent-based techniques, while identifying open challenges.
What carries the argument
Task-oriented taxonomy that divides multi-turn LLM work into instruction following and conversational engagement to organize benchmarks, challenges, and enhancement methods.
Load-bearing premise
The proposed taxonomy captures and organizes essentially all current multi-turn challenges and methods without major omissions as the field develops.
What would settle it
Discovery of a prominent multi-turn interaction problem or method that fits neither the instruction-following category nor the conversational-engagement category would show the taxonomy is incomplete.
read the original abstract
Recent advances in large language models (LLMs) have substantially improved single-turn task performance, yet real-world applications increasingly demand sophisticated multi-turn interactions. This survey provides a comprehensive review of recent progress in evaluating and enhancing multi-turn LLM interactions. Centered on a task-oriented taxonomy-spanning instruction following in domains such as mathematics and coding, and conversational engagement in role-playing, healthcare, education, and adversarial jailbreak settings-we systematically examine the challenges of maintaining context, coherence, fairness, and responsiveness across prolonged dialogues. We organize existing benchmarks and datasets into coherent categories reflecting the evolving landscape of multi-turn dialogue evaluation, and review a broad spectrum of enhancement methodologies, including model-centric strategies (in-context learning, supervised fine-tuning, reinforcement learning, and architectural innovations), external integration approaches (memory augmentation, retrieval-based methods, and knowledge graphs), and agent-based techniques for collaborative interaction. Finally, we identify open challenges and promising directions for future research to further improve the robustness and effectiveness of multi-turn LLM interactions.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims to deliver a comprehensive survey of multi-turn LLM interactions. It introduces a task-oriented taxonomy spanning instruction following (e.g., mathematics, coding) and conversational engagement (role-playing, healthcare, education, adversarial jailbreaks), reviews challenges around context, coherence, fairness and responsiveness, organizes benchmarks and datasets into categories, surveys enhancement methods (model-centric: in-context learning, SFT, RL, architectures; external: memory, retrieval, knowledge graphs; agent-based), and identifies open challenges and future directions.
Significance. If the taxonomy proves comprehensive and the coverage accurate, the survey would be a useful organizing resource for the NLP community. It consolidates work across diverse domains and method classes, potentially helping researchers locate benchmarks, compare enhancement strategies, and identify gaps in multi-turn evaluation. The explicit structure around task-oriented categories and the breadth of reviewed methodologies are strengths that could support standardization efforts in dialogue and agent research.
minor comments (3)
- [Abstract] The abstract states that the survey 'systematically examine[s]' challenges and 'organize[s] existing benchmarks,' but the manuscript would benefit from an explicit statement of the literature search protocol (databases, keywords, cutoff date) to allow readers to assess completeness.
- [Taxonomy] In the section describing the taxonomy, the distinction between 'instruction following' and 'conversational engagement' subcategories is clear at a high level, yet a summary table listing representative benchmarks per leaf category would improve navigability and reduce the risk of perceived omissions.
- [Enhancement methodologies] The review of enhancement methodologies lists model-centric, external, and agent-based approaches; adding a short paragraph comparing their reported gains on shared multi-turn metrics (e.g., coherence scores or task success rates) would strengthen the synthesis.
Simulated Author's Rebuttal
We thank the referee for the positive assessment and recommendation for minor revision. The review accurately captures the scope of our task-oriented taxonomy, the organization of benchmarks, and the coverage of enhancement strategies across model-centric, external, and agent-based approaches. We appreciate the recognition that this structure may aid standardization in dialogue and agent research.
Circularity Check
No significant circularity: survey with no derivations or predictions
full rationale
This is a literature survey paper whose central contribution is a task-oriented taxonomy for organizing existing multi-turn LLM research. The abstract and full text contain no original equations, fitted parameters, predictions, or theoretical derivations. All content consists of reviews of prior benchmarks, datasets, and methods from external sources. No self-citation chains are load-bearing for any claim, and the taxonomy is presented as an organizational framework rather than a derived result. The work is self-contained as a review and introduces no circular reductions.
Axiom & Free-Parameter Ledger
Forward citations
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discussion (0)
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