Evaluating LLMs on Complex Tasks: From Medical Presentations to 3D Simulation

1. Why Complex-Task Evaluation Is Not Just About Correct Answers

As large models enter R&D, office work, design, analysis, and professional production workflows, the focus of evaluation has shifted from “Can it generate fluent text?” to “Can it complete real work under complex constraints?”

Traditional question-answering tests usually require a model to generate an output from a given input. Task boundaries are clear, and results can often be judged against a standard answer. In real work, however, a task usually involves multiple stages:

1. Understand a natural-language request;
2. Break down the goal and create an execution plan;
3. Use calculation, retrieval, coding, or file-processing capabilities;
4. Revise the plan based on intermediate results;
5. Produce a deliverable file or runnable program;
6. Validate results, explain them, and flag risks.

For example, creating a medical presentation is not simply a matter of listing facts. The model must understand the audience, organize the content, handle charts and citations, control the risk of its wording, and ensure clear page hierarchy and accurate terminology. Likewise, completing a 3D simulation task is not only about outputting code. It also involves geometry modeling, parameter setup, debugging, result visualization, and error analysis.

Complex-task evaluation must therefore examine both the final result and the completion process. A seemingly correct answer cannot be judged high quality if it cannot be reproduced, lacks a validation process, or contains safety risks at a critical step.

2. Building a Task Matrix Across Multiple Capabilities

To improve evaluation effectiveness, start by building a task matrix rather than selecting a few questions at random. The matrix should cover different knowledge domains, interaction modes, and failure types.

2.1 Professional Content Generation Tasks

These tasks can ask a model to generate a research outline, technical report, teaching material, or medical presentation on a defined topic. Evaluation should not check only word count. It should also observe:

• Whether the topic is accurate and whether concepts are confused;
• Whether the structure matches the target reader’s cognitive sequence;
• Whether key conclusions are adequately supported;
• Whether facts, inferences, and assumptions are clearly distinguished;
• Whether charts, titles, abstracts, and body text are consistent with one another;
• Whether there are overpromises or inappropriate professional advice.

In high-risk domains, the level of caution in wording is especially important. When information is insufficient, the model should clearly state limitations rather than filling unknown content with a confident tone.

2.2 Programming and Software Engineering Tasks

Programming tasks should approximate a real development workflow rather than test only the generation of a single function. A task can be divided into six stages: requirements analysis, solution design, code implementation, test writing, defect repair, and documentation.

Evaluation should check:

• Whether the code meets functional requirements;
• Whether exceptional inputs are handled;
• Whether data structures and algorithms are reasonable;
• Whether module boundaries are clear;
• Whether there are obvious security risks;
• Whether test cases cover major paths;
• Whether the model can explain key design tradeoffs.

For automated evaluation, prepare both public inputs and hidden inputs. Public inputs check basic functionality, while hidden inputs reveal boundary conditions, excessive resource consumption, insufficient input validation, and similar issues. This reduces the chance that a model earns a high score by memorizing fixed examples.

2.3 Computer Operation and Tool Orchestration Tasks

Computer-operation tasks are closer to real office and production environments. They usually require a model to organize files, process spreadsheets, configure pages, enter data, or perform multi-step software operations.

The key to these tasks is not “clicking speed” but process reliability. Evaluation should record:

• Whether the model correctly understands the target state;
• Whether it confirms important prerequisites before acting;
• Whether it accidentally modifies unrelated files;
• Whether it can detect page changes and abnormal prompts;
• Whether it requests confirmation before irreversible actions;
• Whether it can roll back or replan after a task failure.

Use snapshots in the test environment and record every operation, page change, and final state. For irreversible actions such as deletion, overwriting, submission, and sending, risk control should be scored as a separate item.

2.4 3D Modeling and Simulation Tasks

3D tasks usually test spatial understanding, mathematical reasoning, program generation, and result validation at the same time. A complete task can ask a model to construct a geometric object from textual specifications, set physical parameters, run a simulation, and output visualization results.

Such tasks should not be judged only by whether the final image “looks right.” Evaluation should also check:

• Whether coordinate-system definitions are consistent;
• Whether dimensions, angles, and boundary conditions meet requirements;
• Whether parameter units are unified;
• Whether the model contains intersections, broken faces, or open surfaces;
• Whether the simulation converges;
• Whether results follow basic physical laws;
• Whether error sources and applicable scope are explained.

When no standard answer exists, combine constraint checks, geometric metrics, run logs, and blind expert review. Visual appearance is only one piece of evidence and cannot replace numerical validation.

3. Designing Combined Tests for Eight Complex Capabilities

To prevent evaluation results from being dominated by a single capability, design eight complementary task types:

1. Long-document understanding: tests the ability to extract facts, establish relationships, and locate conflicts;
2. Professional material generation: tests structure design, terminology accuracy, and risk-aware wording;
3. Multi-step computational reasoning: tests formula use, unit conversion, and intermediate-process checking;
4. Code implementation and debugging: tests engineering completeness, testing awareness, and defect repair;
5. File and spreadsheet processing: tests structured data operations and result consistency;
6. Computer interaction: tests environment perception, action planning, and exception recovery;
7. 3D modeling and simulation: tests spatial reasoning, parameter control, and result validation;
8. Integrated project tasks: tests whether a model can connect multiple capabilities into a complete delivery workflow.

Combined tasks are especially valuable because a model may perform well on single-turn questions but degrade noticeably in cross-stage work. For example, it may write correct code but fail to repair it based on runtime errors; generate a complete report but fail to verify the numbers in it; or complete page operations but fail to notice that a previous step has already changed later conditions.

4. Scoring Should Cover Outcomes, Process, and Risk

Complex tasks are not well suited to binary “completed” or “not completed” evaluation. A layered scoring model is preferable.

4.1 Outcome Quality

Outcome quality focuses on whether the deliverable itself meets requirements, including correctness, completeness, usability, and expression quality. For code, check test pass rate, exception handling, and resource consumption. For reports, check factual accuracy, structural completeness, and citation consistency. For simulations, check parameters, geometry, and numerical results.

4.2 Process Quality

Process quality focuses on whether the model took reasonable steps. A model that reaches a correct final result through highly speculative steps is often unlikely to remain stable when task conditions change. Record:

• Whether it clarified requirements first;
• Whether it created an execution plan;
• Whether it validated key assumptions;
• Whether it adjusted based on feedback;
• Whether it preserved necessary intermediate artifacts;
• Whether it completed checks before drawing conclusions.

4.3 Risk Control

Risk control is an essential metric when enterprises use large models. Evaluate in particular whether the model:

• Leaks sensitive information from inputs;
• Performs unauthorized operations;
• Presents uncertain content as established fact;
• Generates code with security vulnerabilities;
• Gives advice beyond its authority in professional settings;
• Recognizes parts it cannot complete reliably.

The overall score can be expressed as:

Overall score = Outcome quality × weight
              + Process quality × weight
              + Risk control × weight
              - Severe-error penalty

In practice, fixed weights should not be applied mechanically. High-risk scenarios should increase the weight of risk control, while exploratory creative tasks may appropriately increase the weight of content quality and creativity.

5. The Evaluation Environment Must Be Reproducible

The same model may produce completely different results under different contexts, tool permissions, and data conditions. Evaluation reports must therefore describe the environment.

At minimum, fix the following variables:

• Input prompts and their versions;
• Available files, data, and tools;
• Maximum execution time;
• Whether internet access or external retrieval is allowed;
• Randomness settings;
• Context-length limits;
• Number of retries after failure;
• Scope of human intervention;
• Output format and acceptance criteria.

For interactive tasks, record every state change. Do not save only the final answer. Preserve the model’s plan, tool calls, error messages, correction process, and final artifacts. This makes it possible to distinguish “insufficient model capability” from “failure caused by environment configuration.”

6. How to Reduce Human Evaluation Bias

Complex tasks often involve subjective judgments, such as whether a page is clear, whether a solution is easy to maintain, or whether a report suits its target readers. The following methods can reduce bias.

6.1 Establish Scoring Rubrics

Break “good quality” into observable indicators. For example, a presentation can be evaluated separately for content accuracy, structural coherence, visual hierarchy, chart effectiveness, and citation completeness.

6.2 Use Two or More Independent Reviewers

Reviewers should score independently first, then re-examine items with large discrepancies. This prevents one reviewer’s personal preference from determining the result.

6.3 Use Blind Review

Hide model identity and generation order to reduce the influence of model names, versions, or prior impressions.

6.4 Combine Automated Checks

Automate whatever can be verified programmatically, such as whether files exist, whether code passes tests, whether spreadsheet totals are consistent, whether geometric objects are closed, and whether output formats meet constraints. Automated checks handle objective aspects, while human review handles semantics, expression, and complex tradeoffs.

7. Common Pitfall: Mistaking Demo Performance for Real Capability

The most common problem in large-model evaluation is replacing systematic conclusions with a few successful cases.

First, a single task cannot represent overall capability. A model may excel in one domain while falling short in long-chain execution, exception handling, or fact verification.

Second, judging only the final output can hide process risks. A model may reach a correct result through an accidental guess, or it may perform dangerous operations in intermediate stages.

Third, ignoring cost and time can overstate practical value. Enterprises care not only about whether an answer is correct, but also about how many interaction rounds, human reviews, and computational resources are required to complete the task.

Fourth, a lack of failure samples can make reports overly optimistic. High-quality evaluation should actively present failure cases and explain where failures occur, whether they can be improved through prompts, and how tool constraints or workflow design can reduce risk.

8. An Enterprise-Ready Evaluation Workflow

An executable evaluation project can usually be divided into five stages.

Phase 1: Define Business Goals

First determine what problem the model should solve and what result is acceptable. Do not start from “what the model can do.” Start from real bottlenecks in the workflow.

Phase 2: Build a Representative Task Set

Extract desensitized cases from actual work, covering common tasks, edge tasks, and high-risk tasks. More tasks are not necessarily better; tasks must be representative and repeatable.

Phase 3: Define Acceptance Criteria

For each task, specify input conditions, allowed tools, output format, quality metrics, and failure criteria. For more open-ended tasks, set a minimum passing line and a list of severe errors.

Phase 4: Run Multiple Test Rounds

Do not test only once. Run multiple rounds with different difficulty levels, data distributions, and contexts to observe result stability, failure types, and recovery capability.

Phase 5: Produce an Evaluation Report

The report should include at least task descriptions, environment configuration, scoring rules, overall results, breakdown results, typical success cases, typical failure cases, and improvement recommendations. When model iterations are involved, retain version comparisons and avoid showing only the most favorable run.

9. Conclusion: From Model Rankings to Task Reliability

The value of a complex large model cannot be summarized simply as being “smarter.” What matters is whether it can understand real goals, execute reliably with tools and constraints, detect and correct errors in time, deliver results that people can continue to use, and remain sufficiently cautious under uncertainty.

Future evaluation systems should therefore move from single-turn question answering to complete tasks, from a single score to multidimensional metrics, from final answers to process evidence, and from showcasing best cases to analyzing failure boundaries. Only with transparent, reproducible, and explainable evaluation processes can developers and enterprise users judge whether a model is truly ready for production and design appropriate human-AI collaboration mechanisms accordingly.