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LLMs Get Lost In Multi-Turn Conversation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 8.0Download Report PDF
multi-turnunderspecificationllm simulation
Abstract:

Large Language Models (LLMs) are conversational interfaces. As such, LLMs have the potential to assist their users not only when they can fully specify the task at hand, but also to help them define, explore, and refine what they need through multi-turn conversational exchange. Although analysis of LLM conversation logs has confirmed that underspecification occurs frequently in user instructions, LLM evaluation has predominantly focused on the single-turn, fully-specified instruction setting. In this work, we perform large-scale simulation experiments to compare LLM performance in single- and multi-turn settings. Our experiments confirm that all the top open- and closed-weight LLMs we test exhibit significantly lower performance in multi-turn conversations than single-turn, with an average drop of 39% across six generation tasks. Analysis of 200,000+ simulated conversations decomposes the performance degradation into two components: a minor loss in aptitude and a significant increase in unreliability. We find that LLMs often make assumptions in early turns and prematurely attempt to generate final solutions, on which they overly rely. In simpler terms, we discover that when LLMs take a wrong turn in a conversation, they get lost and do not recover.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper investigates performance degradation in multi-turn underspecified conversations through large-scale simulation experiments across six generation tasks. It resides in the 'Performance Degradation and Error Analysis' leaf, which contains only three papers total, making this a relatively sparse research direction within the broader taxonomy of 50 papers. The two sibling papers address adversarial vulnerabilities (Multi Turn Jailbreaks) and training-time mitigation (Verifiable Accuracy Rewards), whereas this work focuses on systematic diagnostic analysis of failure modes. This positioning suggests the paper targets an underexplored niche: empirical characterization of how and why LLMs fail in extended underspecified dialogues.

The taxonomy reveals substantial activity in adjacent areas. The 'Benchmark Design and Evaluation Frameworks' branch contains 19 papers across four leaves, including general dialogue benchmarks (8 papers) and domain-specific evaluations (6 papers). The 'Ambiguity and Underspecification Handling' branch (13 papers) addresses clarification strategies and query resolution, representing a complementary perspective focused on mitigation rather than diagnosis. The paper's analytical approach bridges these areas: it evaluates performance degradation (its home leaf) while examining underspecification handling (a neighboring branch), but does so through diagnostic lens rather than proposing new clarification mechanisms or benchmarks.

Among 30 candidates examined across three contributions, none were identified as clearly refuting the work. The sharded simulation environment examined 10 candidates with no refutations; the aptitude-unreliability decomposition framework examined 10 candidates with no refutations; and the large-scale empirical study examined 10 candidates with no refutations. This suggests that within the limited search scope, the specific combination of simulation-based methodology, performance decomposition framework, and scale of empirical analysis (200,000+ conversations) appears distinctive. However, the search examined only top-30 semantic matches, leaving open whether more exhaustive literature review might surface closer prior work in simulation methodologies or decomposition frameworks.

Based on the limited search scope of 30 candidates, the work appears to occupy a relatively novel position at the intersection of performance analysis and underspecification handling. The sparse population of its home leaf (3 papers) and absence of refuting candidates suggest the diagnostic framing and decomposition approach may be distinctive contributions. However, the analysis cannot rule out relevant prior work outside the top-30 semantic matches, particularly in adjacent areas like benchmark design or training optimization where methodological overlaps might exist.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: multi-turn underspecified conversation performance evaluation. This field examines how conversational systems handle extended interactions where user intent is incomplete, ambiguous, or evolving across turns. The taxonomy organizes research into six main branches. Benchmark Design and Evaluation Frameworks (e.g., AgentBoard[3], ConvBench[26]) establish standardized testbeds for measuring multi-turn capabilities. Ambiguity and Underspecification Handling (e.g., Query Resolution[4], Ask to Clarify[39]) focuses on methods for detecting and resolving unclear user requests through clarification strategies. Performance Degradation and Error Analysis investigates how and why systems fail as conversations lengthen, including adversarial scenarios like Multi Turn Jailbreaks[14]. Training and Optimization Methods (e.g., CPO[9], Verifiable Accuracy Rewards[31]) develop techniques to improve model robustness in extended dialogues. Conversational Modeling Approaches explores architectural choices for maintaining context and coherence, while Domain-Specific Applications (e.g., Zhongjing[23], CPsyCoun[24]) adapt these techniques to specialized settings like healthcare or customer service. A central tension emerges between proactive clarification strategies and passive context accumulation: some works emphasize explicit question-asking to resolve ambiguity early (InfoQuest[11], Question Clarification[38]), while others focus on implicit context modeling that infers intent from dialogue history (ContextQFormer[46], MTRAG[40]). Lost In Conversation[0] sits squarely within the Performance Degradation and Error Analysis branch, examining how conversational systems deteriorate over extended interactions. Its emphasis on diagnosing failure modes complements nearby work like Verifiable Accuracy Rewards[31], which addresses degradation through training-time interventions, and Multi Turn Jailbreaks[14], which explores adversarial vulnerabilities. Where these neighbors focus on mitigation or exploitation of weaknesses, Lost In Conversation[0] provides systematic analysis of when and why multi-turn underspecification leads to breakdowns, offering diagnostic insights that inform both benchmark design and optimization strategies across the broader landscape.

Claimed Contributions

Sharded simulation environment for multi-turn underspecified conversations

The authors develop a simulation framework that transforms single-turn instructions into sharded instructions, revealing information gradually across conversation turns. This enables large-scale evaluation of LLMs in multi-turn, underspecified settings using existing benchmarks.

10 retrieved papers
Decomposition of performance degradation into aptitude and unreliability

The authors introduce metrics to separate LLM performance drops into aptitude (best-case capability) and unreliability (variance across runs). They find that multi-turn degradation stems primarily from increased unreliability rather than aptitude loss.

10 retrieved papers
Large-scale empirical study revealing multi-turn performance degradation

The authors conduct over 200,000 simulated conversations across 15 LLMs and six tasks, demonstrating consistent and substantial performance drops in multi-turn settings. This empirical finding establishes the 'lost in conversation' phenomenon across state-of-the-art models.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Sharded simulation environment for multi-turn underspecified conversations

The authors develop a simulation framework that transforms single-turn instructions into sharded instructions, revealing information gradually across conversation turns. This enables large-scale evaluation of LLMs in multi-turn, underspecified settings using existing benchmarks.

Contribution

Decomposition of performance degradation into aptitude and unreliability

The authors introduce metrics to separate LLM performance drops into aptitude (best-case capability) and unreliability (variance across runs). They find that multi-turn degradation stems primarily from increased unreliability rather than aptitude loss.

Contribution

Large-scale empirical study revealing multi-turn performance degradation

The authors conduct over 200,000 simulated conversations across 15 LLMs and six tasks, demonstrating consistent and substantial performance drops in multi-turn settings. This empirical finding establishes the 'lost in conversation' phenomenon across state-of-the-art models.

Table of Contents