Lost in Evaluation: Rethinking Multi-Turn Failures in LLMs

Understanding Multi-Turn Failures in LLMs

Multi-turn interactions with large language models (LLMs) are becoming increasingly central to applications such as conversational AI, code generation, and task automation. These extended exchanges go far beyond single-question prompts, requiring LLMs to maintain context, understand user intent across turns, and deliver consistently relevant responses. However, despite the remarkable progress in LLM capabilities, multi-turn dialogues often break down in ways that single-turn evaluations fail to capture.

At the heart of these breakdowns is the context retention challenge. LLMs have a finite memory window, causing them to lose track of earlier details as conversations grow lengthy. For instance, in technical troubleshooting scenarios where earlier context is crucial, LLMs might overlook critical information referenced many turns back, leading to inconsistencies or incorrect instructions. Researchers at Google DeepMind have explored context length limits and found that attention span directly affects multi-turn performance.

Another common failure involves reference ambiguity. In dialogue, users often refer back to previous messages using pronouns or shorthand (e.g., “Can you do what I asked earlier?”). Successful navigation of such references depends on the LLM’s ability to track conversational history accurately. When LLMs incorrectly resolve pronouns or misinterpret user intentions, the conversation can become confusing or off-topic. This is discussed in detail by researchers at Meta AI, emphasizing the importance of long-term memory in dialogue systems.

A further dimension to multi-turn failures is cumulative error propagation. Small inaccuracies in early responses can escalate over several turns, leading the interaction further astray. For example, if an error in a mathematical calculation goes uncorrected, subsequent steps—though logically consistent with the mistaken output—become increasingly detached from the original intent or correct answer. This compounding effect is explored in evaluation frameworks like Multi-turn Evaluations for LLMs, which stress the need for multi-turn benchmarks rather than isolated prompt tests.

The subtleties of human conversation, such as implicit requests, sarcasm, or stylistic continuity, also trip up LLMs. Over multiple turns, maintaining tone and recognizing subtle shifts in user expectations requires nuanced understanding. Conventional benchmarks often neglect these aspects, leading to a misalignment between benchmark performance and real-world usability. Studies like those by Microsoft Research underscore the necessity for holistic evaluations to expose such failures.

In summary, multi-turn failures in LLMs stem from limitations in context retention, handling ambiguous references, error accumulation, and conversational subtlety. These issues highlight the pressing need to rethink how we evaluate LLMs to ensure robust and reliable performance in practical, real-world conversations. Addressing these challenges is a foundational step toward creating more effective and trustworthy conversational AI systems.

Examples of Multi-Turn Evaluation Challenges

Multi-turn evaluation challenges arise when large language models (LLMs) engage in conversations that extend beyond a single prompt and response. In these interactions, the model must maintain context, remember earlier turns, and generate coherent, contextually appropriate replies. While single-turn evaluation may demonstrate impressive capabilities, nuanced challenges frequently surface in longer, more complex exchanges.

Consider the following detailed examples that highlight the intricacies of multi-turn evaluation:

1. Context Retention Over Multiple Turns

One persistent challenge is ensuring that the LLM accurately remembers relevant information from earlier in the conversation. For example, in a medical consultation scenario, the user might mention a specific allergy in the first turn. Later, when querying about potential medications, the LLM must recall this detail to offer safe advice. Numerous studies, such as those by the Allen Institute for AI, demonstrate that even state-of-the-art models sometimes miss nuanced details when conversation history grows longer, leading to critical mistakes or omissions.

Step-by-step example: A patient states they are allergic to penicillin in their initial message. Several turns later, the LLM suggests antibiotics without explicitly accounting for this allergy, risking unsafe recommendations.

2. Consistency in Persona and Instructions

Maintaining a consistent persona or adhering to instructions across multiple turns is another complex evaluation point. As highlighted by research from ACL Anthology, LLMs may inadvertently shift tone, role, or obey initial directives inconsistently. For instance, a user might request responses in the “style of Shakespeare,” yet after several exchanges, the model’s output reverts to generic, modern English.

Example: At the outset, a user asks for a literary review in the voice of Ernest Hemingway. The LLM begins appropriately, but as the dialogue progresses, the responses lose the distinctive style, breaking the illusion and revealing evaluation gaps.

3. Handling Contradictions and Clarifications

Multi-turn scenarios often include clarifying corrections or contradictory statements from the user. An effective model is expected to reconcile these changes seamlessly. However, studies by experts at Google Research show that LLMs occasionally fail to recognize updated user intents or resolve contradictions, leading to misleading or incorrect responses.

Clarification scenario: The user asks for vegetarian meal ideas. Midway, they clarify that they also eat fish. The ideal LLM should update its responses, but sometimes it continues presenting strictly vegetarian suggestions, ignoring the new information.

4. Evaluation of Long-Form Tasks

Tasks such as brainstorming, summarizing extended dialogues, or adapting to shifting objectives are especially prone to multi-turn failures. When required to summarize or make decisions based on extended chat logs, LLMs may struggle to prioritize information or understand subtle shifts in conversational goals. The Stanford Human Evaluation of LLMs notes that summarization over many turns often leads to oversights or factual errors.

Task-based example: During collaborative writing, the LLM co-authors a story over 15 turns. By the end, plot threads or character personalities might become inconsistent, highlighting a breakdown in multi-turn comprehension and evaluation.

Evaluating LLMs in these contexts requires sophisticated assessment strategies that go beyond simple accuracy metrics, incorporating broader measures of consistency, memory, adaptability, and contextual awareness. For a deeper dive into evaluation frameworks, resources like the OpenAI technical guides and industry benchmarks offer valuable insights.

Why Traditional Evaluation Falls Short

Conventional approaches to evaluating large language models (LLMs) have typically relied on single-turn benchmarks or static question sets, measuring how well models deliver concise, correct answers in isolation. While this strategy offers a straightforward metric for progress, it loses sight of the true complexity of language: sustained, coherent, and contextually aware conversations. The pitfalls of this traditional evaluation are increasingly evident as LLMs are called upon to handle multi-turn dialogues in real-world applications, from customer service to collaborative writing.

One key limitation is the failure to assess context retention and long-term coherence. In multi-turn interactions, understanding hinges not just on the current question, but also on information exchanged over many previous turns. Yet, classic benchmarks rarely test a model’s ability to track nuances and references—or to avoid contradictory statements—as dialogue progresses. For example, a user clarifies their preferences in one turn, but if the model cannot remember these details two turns later, the entire exchange breaks down. Research from ACM Digital Library highlights how successful multi-turn understanding is a far higher bar than one-shot answers, demanding evaluation routines that reflect these challenges.

Another key gap lies in the evaluation of interactive repair and adaptation. Humans routinely revise their contributions and rectify misunderstandings by revisiting prior dialogue. Traditional evaluations, however, rarely measure this capability. If a model answers inaccurately, can it recognize the mistake when prompted and adapt accordingly in the next turn? Single-question benchmarks won’t capture such self-correcting behaviors. Google AI’s research on LaMDA details the difficulties in designing tests that capture this iterative conversational nature, illustrating why static question-and-answer models leave much to be desired.

Further complicating matters is the risk of evaluation leakage. When test benchmarks are static and widely available, models may become optimized for these tests—either through explicit training or inadvertent overfitting—without genuinely improving at general conversation. The result is a detachment between reported scores and the genuine conversational capability of LLMs in complex, open-ended scenarios. The dangers of such overfitting are outlined by Nature, which cautions against benchmarks that are too predictable or surface-level.

In sum, traditional methods are inadequate for probing the multi-faceted, dynamic demands of multi-turn dialogue. Progress in LLM evaluation depends on designing tests that mirror actual usage: persistent context, adaptability, error recovery, and long-term coherence. Without such innovation, benchmark results risk providing false reassurance about model competence in real-world settings, and the field stays “lost in evaluation.”

The Impact of Context Limitation on LLM Performance

Large language models (LLMs) such as GPT-4 or Google’s Bard have achieved remarkable progress in simulating human-like dialogue and generating coherent, contextually appropriate responses. However, one persistent challenge—especially noticeable across multi-turn interactions—is the impact of context limitations on LLM performance. Context limitation refers to the model’s inherent constraint in processing and “remembering” a fixed amount of preceding conversation, which can lead to several nuanced failures over extended exchanges.

Understanding Context Windows in LLMs

LLMs utilize what is called a “context window” to understand and generate text. This window determines how many previous words, sentences, or tokens the model can reference at one time. For example, most state-of-the-art models have a context window that ranges from thousands to tens of thousands of tokens. Once a conversation exceeds that limit, older exchanges are truncated or omitted, leading to potential lapses in understanding or continuity. The detailed technicals of this limitation are well described in OpenAI’s technical note on token limits.

Real-World Effects of Context Limitations

Loss of Conversation History: In customer support chatbots or digital assistants, losing track of earlier conversation threads can result in generic or repetitive answers, frustrating users who expect continuity. This phenomenon is analyzed in research published by Semantic Scholar, which examines how omitted context disrupts information flow.
Diminished Personalization: When the model fails to recall prior user details or preferences in a multi-turn dialogue, it cannot deliver tailored recommendations or maintain a sense of ongoing rapport. Over the course of, say, an hour-long tutoring session, the model might forget earlier answers or hints, resulting in redundancy or error.
Compounding Errors: As context drops off, the likelihood of misinterpretation increases. For example, a model might lose the thread of a technical problem-solving session, thereby compounding minor misunderstandings into larger mistakes.

Strategies to Mitigate Context Limitations

Researchers and practitioners employ several strategies to cope with and mitigate the impact of context limitations:

Conversation Summarization: Periodic summarization of prior turns can compress historical context, helping LLMs maintain coherence across long conversations. Approaches and experiments are outlined in detail by ACL Anthology.
External Memory Augmentation: Augmenting LLMs with an external memory system, such as retrievable document stores or session logs, is an active area of research. This method enables the model to “look up” relevant facts or past exchanges that have fallen outside the current window.
User Input Engineering: Developers are advised to design applications that proactively steer users to reference earlier parts of the conversation, leveraging explicit recall techniques or prompts.

Case Example: Technical Support Chatbot

Consider a user seeking technical support over a prolonged chat session. As the troubleshooting back-and-forth grows longer, the LLM may lose context about previously tried solutions, or even prior error messages—resulting in unhelpful loops or repeated troubleshooting steps. This scenario exemplifies how premature context truncation can degrade user experience and reduce the perceived intelligence of the model. Such situations highlight the practical importance of addressing context loss, as noted in ZDNet’s technical coverage on context management in conversational AI.

In multi-turn scenarios, the impact of context limitation is profound—not just for conversational coherence, but for long-term trust and satisfaction in AI applications. Therefore, as we scale up LLM capabilities, deliberate engineering around context window constraints is paramount to ensure that advanced LLMs remain both helpful and reliable over extended interactions.

Human vs. Model: Differences in Multi-Turn Comprehension

When evaluating multi-turn conversations, the contrast between human comprehension and the performance of large language models (LLMs) becomes starkly apparent. Humans naturally excel at integrating context and intent over extended dialogues, often recalling subtle cues, implied meanings, and the subjective nuances of prior exchanges. In contrast, LLMs, while impressively capable, still struggle with holistic understanding, especially as conversations grow in complexity and length.

Context Retention and Recall

Humans can effortlessly retain context in conversation, even picking up abandoned threads after several turns—a friend referencing an inside joke or a teacher revisiting a concept covered earlier. This ability stems from our episodic memory and shared experience frameworks, allowing us to personalize responses and infer unstated details. For instance, in therapeutic sessions, counselors gauge a client’s progress based on prior disclosures, using accumulated context to guide empathetic, targeted responses.

Conversely, LLMs typically face challenges with context retention. Although models like GPT-4 feature advanced token windows, they are fundamentally limited compared to human cognitive capabilities. Failures often manifest as loss of topical cohesion or forgetting prior user-supplied information. For an in-depth understanding of context limitations in LLMs, see the research analysis by ArXiv: On the Limits of Context in LLMs. These breakdowns are especially visible in long-chain tasks such as story continuation or customer service dialogues, where earlier points get muddled or dropped altogether.

Inference and Nuance

Another key difference is in inference: humans excel at reading between the lines. Social context, humor, sarcasm, and personal backstories all shape our conversational responses. For example, a coworker’s ambiguous hint might be enough for you to deduce underlying stress, adjusting your approach compassionately. Studies in cognitive science highlight how theory of mind skills enable people to anticipate others’ thoughts and emotions in multi-turn exchanges.

LLMs, trained largely on surface-level text features, frequently miss subtext or implied meanings, handling most exchanges literally unless specifically signaled otherwise. While prompt engineering and fine-tuning have introduced some sensitivity to nuance, true theory of mind remains an ongoing research frontier. Models may deliver plausible but contextually tone-deaf outputs, highlighting gaps in deeper comprehension.

Error Recovery Strategies

Humans are adept at identifying and recovering from misunderstandings. If context is lost, we backtrack, seek clarification, or prompt others to elaborate. Multi-turn interactions with LLMs, however, can deteriorate rapidly if an error goes uncorrected: one misunderstood answer can snowball, leading to a cascade of off-topic or inaccurate replies. As detailed by Harvard Data Science Review, such failures in LLM conversation management highlight the need for better iterative self-correction and user-guided intervention mechanisms.

Ultimately, while LLMs showcase remarkable fluency and can mimic coherent dialogue for several turns, the subtle, adaptive, and context-rich capabilities that humans bring to multi-turn comprehension remain unmatched. Recognizing these differences is crucial when designing evaluation benchmarks and deploying conversational AI in real-world applications.

Metrics Commonly Used—and Their Limitations

When evaluating the performance of large language models (LLMs) in complex, multi-turn conversations, the choice of metric fundamentally shapes both our understanding and expectations. Despite the impressive advancements in natural language processing, the metrics we commonly rely on present intrinsic challenges in accurately capturing the nuanced failures and strengths of LLMs across multiple dialogue turns.

Common Evaluation Metrics:

Accuracy and Exact Match: Accuracy and exact match metrics assess whether the model’s response precisely matches a reference output. While effective for tasks with clear right-wrong answers (like factual Q&A), these metrics fall short in evaluating more open-ended or context-rich dialogue scenarios.
BLEU and ROUGE: Originally designed for tasks such as machine translation and summarization, BLEU and ROUGE calculate word and phrase overlap with a reference. However, these surface-level syntactic comparisons often overlook the deeper semantic appropriateness required in multi-turn conversations, resulting in misleadingly low or high scores depending on minor wording variations.
Human Evaluation: The gold standard—human raters—are employed to judge outputs along dimensions such as relevance, coherency, and engagement, as highlighted in studies by DeepMind. However, this approach is labor-intensive, costly, and may suffer from subjectivity or inconsistency, especially at scale.

Limitations in Multi-Turn Settings:

Context Sensitivity: Most automated metrics struggle to track context across multiple turns. For instance, a model’s error at turn 3 may propagate, negatively affecting all subsequent responses. Yet, traditional metrics often only assess individual turns in isolation, missing these cascades of error.
Semantic Nuance: In real conversations, there may be multiple valid responses, especially in creative or opinion-based tasks. Metrics like BLEU can unfairly penalize outputs that, while different in surface form, are still contextually valid. See this detailed analysis in ACM Digital Library.
Failure Identification: Many metrics are not designed to diagnose why an LLM fails in multi-turn interactions—whether due to memory limitations, misunderstanding intent, or failure to maintain consistency over time. For example, a model may slip from referring to a subject as plural to singular, causing confusion, yet this will not necessarily be reflected in metrics focused on factual accuracy alone.

To move beyond these limitations, researchers are turning to more sophisticated methods, such as interactive evaluation frameworks and layered annotation schemes that account for conversational context, user intent, and longitudinal coherency. The imperative is clear: as LLMs are tasked with increasingly complex, real-world dialogues, our evaluation methods must evolve in parallel to illuminate subtle but crucial multi-turn failures.

Case Studies: Real-World Multi-Turn Failures

In exploring the practical consequences of multi-turn failures in large language models (LLMs), real-world case studies provide crucial insights into where and why these systems break down. Below, we examine specific categories of such failures, illustrated with detailed examples and supporting evidence from leading research and technology institutions.

1. Breakdown in Context Carryover: Customer Support Chatbots

Multi-turn conversations often hinge on the seamless retention of context between exchanges. However, LLM-driven chatbots can struggle with this continuity, leading to misunderstandings or incorrect guidance. For instance, in a tech support scenario, a user might inquire about resetting a password and then follow up with account security questions. Many models fail to connect the dots, treating each inquiry in isolation.

Step 1: User requests help resetting a password.
Step 2: The bot provides instructions, but if the user later asks, “Is my account safe now?” the model may not recall the prior reset action and simply provide generic account safety tips instead of reassurance based on previous steps.

Such lapses can erode user trust. For further analysis, consider the 2021 ACM study on dialogue state tracking, which documents context retention as a major hurdle for conversational AI in multi-turn settings.

2. Compounding Errors in Collaborative Writing Tools

LLMs are frequently deployed in collaborative writing applications, assisting with content creation over several interactions. In these contexts, seemingly minor misunderstandings can accumulate and derail the overall direction.

Step 1: Author and LLM co-write an article draft. The user requests a paragraph about recent AI breakthroughs in healthcare.
Step 2: The LLM adds text, but mistakenly blends healthcare and unrelated financial technology examples due to ambiguous prior turns.
Step 3: Over subsequent edits, these confusions magnify, leading to a muddled article that strays from the intended topic.

This phenomenon, referred to as “error propagation,” is well-documented by the Harvard Data Science Review, stressing the importance of rigorous checkpointing and review in multi-turn assisted writing.

3. Misalignment in Task-Oriented Dialogue: Virtual Health Assistants

Task-oriented dialogue systems, such as virtual health assistants, must maintain precise, multi-turn communication to deliver effective care instructions. A common failure mode occurs when the LLM loses track of patient-provided information over several conversational rounds.

Step 1: A patient describes two symptoms across different messages.
Step 2: The assistant, failing to integrate these symptoms, offers advice only based on the most recent input, potentially overlooking critical information.

The risk here isn’t just inconvenience—it could directly affect patient health outcomes. Analysts from Mayo Clinic Proceedings highlight such risks in a comprehensive review of AI-driven healthcare applications.

4. Loss of User Intent: Educational Tutoring Platforms

When deployed as digital tutors, LLMs must accurately track and build upon a student’s evolving understanding over numerous exchanges. A recurring failure occurs when the model loses track of the user’s intent or learning goal midway.

Step 1: Student requests help solving quadratic equations.
Step 2: After a few successful hints, the student subtly shifts focus to ask about graphing solutions.
Step 3: The LLM, not detecting the shift, continues providing algebraic solutions rather than instructional content on graphing.

This vulnerability is tracked in research from EDUCAUSE, where case studies emphasize tailored feedback loops and improved context tracking in educational AI.

These real-world cases illuminate how multi-turn failures in LLMs can cascade from minor context lapses into significant breakdowns in user satisfaction and outcomes. Ongoing research and robust evaluation frameworks, such as those proposed by recent preprints on arXiv, are critical for mitigating these challenges and driving the next wave of conversational intelligence.

Strategies for Improving Multi-Turn Evaluations

Improving the evaluation of multi-turn interactions in large language models (LLMs) requires a shift from traditional, single-response benchmarks towards more dynamic and robust strategies. Multi-turn conversations present unique challenges, including context retention, logical consistency across turns, and user intent comprehension. Here’s how the field can move forward to better capture the nuances of these interactions:

1. Develop Comprehensive Multi-Turn Benchmarks

One effective strategy is to create dedicated benchmarks that focus exclusively on multi-turn exchanges. Unlike single-turn datasets, these benchmarks should simulate real-world conversational dynamics where context and memory are crucial. For inspiration, consider how the Dialogue Natural Language Inference (DNLI) dataset was designed, emphasizing context-dependent reasoning. By crafting scenario-based benchmarks rooted in authentic user tasks, developers can better pinpoint where and how LLMs falter over several conversational turns.

2. Human-in-the-Loop Evaluation

Automated metrics often fail to capture the intricacies of multi-turn conversations, such as subtle errors in context or emotional tone. Involving humans in the evaluation process, as seen with initiatives like Meta’s human evaluation frameworks, ensures that assessments include nuanced understanding of coherence, relevance, and engagement. Setting up panels of diverse evaluators and establishing clear rubrics for multi-turn coherence, recall, and responsiveness can significantly enhance the reliability and validity of evaluation outcomes.

3. Dynamic Scenario Testing

To push LLMs beyond scripted datasets, dynamic scenario testing introduces unpredictability similar to real conversations. For example, a user might suddenly reference an earlier point or change topics—a common occurrence in customer support or educational applications. Rigorous scenario testing, as utilized in advanced chatbot competitions like those described in Google’s conversational quality research, challenges models to demonstrate memory retention and adaptive reasoning, hallmarks of truly intelligent systems.

4. Longitudinal Analysis of Model Performance

It’s critical to examine model performance over extended conversations rather than just isolated turn pairs. Employing longitudinal studies, where the same conversational trajectory is tracked over time, can unearth degradation in coherence or increased hallucinations. This method, discussed in depth in the Harvard Data Science Review, allows teams to identify and address when and why models lose track, guiding targeted fine-tuning strategies.

5. Incorporate Adaptive User Feedback Loops

Embedding real-world feedback mechanisms—where users rate or correct outputs in ongoing multi-turn conversations—enables models to learn from genuine mistakes in context. For instance, platforms like OpenAI have experimented with—in training and deployment—feedback loops that inform reinforcement learning for continuous model improvement. Such iterative, user-centered evaluation ensures that LLMs adapt to changing usage patterns and user expectations.

By systematically adopting these strategies, organizations and researchers can uncover deeper insights into LLM multi-turn failures and craft evaluation protocols that foster the development of more context-aware, reliable, and effective language models for conversation-heavy applications.

Future Directions in Multi-Turn Dialogue Evaluation

As dialogue systems powered by large language models (LLMs) grow more advanced, the evaluation of multi-turn interactions must also evolve. Current approaches tend to evaluate these systems using isolated conversational snippets or focus on single-turn exchanges, often overlooking the complex, nuanced failures that only manifest across several turns. Moving ahead, reshaping how we assess multi-turn dialogue is essential not just for capturing surface-level accuracy but for truly understanding the model’s conversational depth and resilience.

Incorporating Contextual and Sequential Assessment

One promising direction is the integration of context-aware evaluation metrics that can trace the flow and consistency of dialogue over multiple exchanges. Traditional metrics like BLEU or ROUGE (Papineni et al., 2002) are limited in their ability to account for context spanning several turns. To address this, researchers are exploring metrics that directly model dependencies and context retention, such as entity tracking or dialogue state consistency. For example, by evaluating whether an LLM maintains factual consistency about user preferences throughout a conversation, evaluators get a clearer picture of long-term coherence. Standardized datasets such as MultiWOZ (MultiWOZ dialogue dataset) are invaluable for testing these capabilities at scale.

Real-Time User Feedback Loops

Moving beyond static, offline evaluation, real-time user interactions and feedback loops offer a rich avenue for assessing the practical usability of LLM dialogue. Gathering insights on user perceptions — not just post-hoc, but during the conversation — allows developers to capture subtle breakdowns in trust, misunderstanding, or user satisfaction. Embedding mechanisms for immediate user correction empowers systems to learn from naturalistic, organic errors. Recent experimentation, such as Microsoft’s RealFeedback framework (Microsoft RealFeedback), demonstrates the value of incorporating authentic user responses into the training and evaluation pipeline.

Scenario-Based Stress Testing

Another critical methodology is to employ scenario-based stress testing. By simulating complex, edge-case conversations rich with ambiguous or contradictory information, evaluators can systematically probe the resilience of LLMs. For instance, models can be challenged with scenarios where prior conversation context is intentionally misleading or where the user switches topics rapidly. These tests expose vulnerabilities and force LLMs to navigate the kind of conversational traps that are pervasive in real-world deployments. IBM Research’s work on adversarial dialogue evaluation (IBM Research AI Dialogue) provides a blueprint for developing such rigorous test suites.

Multi-Perspective Human Judgement

Relying solely on automated metrics risks missing out on the human nuances of dialogue. Therefore, engaging diverse human evaluators, including domain experts, end-users, and even individuals from varied cultural backgrounds, is crucial for holistic assessment. Multi-perspective judgement helps surface issues such as cultural insensitivity, implicit bias, or subtly incoherent dialogue that may escape statistical analysis. The collaborative evaluation frameworks advocated by organizations like the Allen Institute for AI (Allen Institute for AI) emphasize the need for structured, qualitative feedback as a complement to quantitative scores.

Towards Continuous, Transparent Reporting

The future also lies in continuous reporting and transparency. Openly sharing multi-turn evaluation results, annotated failure cases, and ongoing progress fosters both accountability and community-driven improvement. Efforts such as Hugging Face’s Evaluate Hub are promoting transparency by making evaluation outcomes easily accessible and encouraging community contributions to metric development.

In sum, advancing multi-turn LLM evaluation demands a concerted embrace of longer context, real user feedback, scenario analytics, diverse human insight, and open transparency. Only by expanding our evaluative lens can we truly address the subtle, layered failures that arise during complex human-machine conversations, paving the way for more reliable and human-centric dialog agents.