Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback

• IGLU challenges AI agents to interpret complex spatial instructions and place blocks in a Minecraft-like 3D world, where performance remains below human ability.Source: §1 · p. 1
• The proposed interaction changes IGLU from a single-step evaluation into back-and-forth collaboration between human Architects and AI Builders.The framework overview depicts initial instructions, block placement, human help after mistakes, and clarification-driven assistance.Source: §1 · pp. 1–2, Figure 2 · p. 2
• The framework lets human Architects correct Builder mistakes through four high-level forms of help rather than specifying every block location directly.Help can improve current predictions, support future learning during training, and remain simple for humans to provide.Source: §1 · p. 2
• Agents can self-identify confusion by generating candidate help, then ask clarification questions about concepts they do not understand.Human responses can subsequently be incorporated as help for the task.Source: §1 · p. 2
• Experiments show performance improvements, supporting the paper’s goal of a fully interactive agent for tasks like IGLU.Source: §1 · p. 2

Human-AI Interaction Tasks

• Human-AI interaction research addresses agents that understand complex language, spatial orientation, and unseen concepts while collaborating on real-world tasks.Source: §2 · p. 2

IGLU Task

• The paper studies a dialogue-only IGLU setup in which models predict placed blocks from language, without reinforcement learning or vision, limiting direct comparison with existing baselines.Source: §2 · p. 3

User-Feedback

• The work extends regional and directional hints from Mehta and Goldwasser with additional feedback types, a harder task, and a stronger language-model baseline.Source: §2 · p. 3

Clarifying Questions

• Clarification-question research motivates using help to detect model confusion and determine what question to ask.Source: §2 · p. 3

Task Formulation

• Given dialogue history, the final Architect instruction, and a language representation of the Minecraft world state, the Builder predicts block coordinates.The setup uses a human Architect, an AI Builder, and no vision component.Source: §3 · p. 3

Model Architecture

• The baseline is a BART-base Transformer trained for conditional generation, with natural-language inputs and outputs.Source: §3 · p. 3
• Grid coordinates are represented by distances and directions from the origin, using left/right, up/down, and higher/lower language labels.Source: §3 · p. 3
• Multiple blocks are encoded as multiple coordinate-description sentences, while outputs that violate the required format are ignored as invalid.Source: §3 · p. 3

4 Help-Specific Models

• The resulting interaction lets agents receive human help, seek clarification when confused, and sometimes improve performance through self-generated help without human intervention.The framework is designed as a reciprocal interaction rather than a single instruction followed by one prediction.Source: §4 · p. 4, §4.4 · pp. 5–6

4.1 Help Types

• Four help types—restrictive, length-based, corrective, and mistake-based—provide high-level guidance about search regions, block counts, directional adjustments, or placement errors.The help is intended to simplify the task without directly specifying the final block placement.Source: §4 · p. 4, §4.1 · p. 4

4.2 Forming Help in Language

• Synthetic slot-filled utterances generate help without human input, while LLM-generated variants simulate additional human language diversity.Help values are derived from the appropriate region or other task information, with different language used at training and test time.Source: §4.2 · p. 4

4.3 Incorporating Help

• The Builder receives help by appending it as a natural-language sentence to the IGLU instruction, whereas pre-training help representations worsened results because BART could not incorporate the pre-trained layers successfully.The input format combines the instruction and help, for example, “INSTRUCTION:..., HELP: ...”.Source: §4.3 · p. 5

4.4 Using Help for Clarifying Questions

• The agent detects confusion when self-generated help substantially changes its prediction, then asks a clarification question tied to the most influential help type.It first generates an initial prediction, iterates over help types, compares outputs against a threshold, and slot-fills a corresponding question.Source: §4.4 · pp. 5–6

4.4.1 Self-Generated Help Models

• Self-generated help uses classification models to predict task-relevant guidance before the more complex block-placement prediction.The classifiers predict regions, block counts, directions, or adjustment counts, depending on the help type.Source: §4.4 · p. 5, §4.4.1 · p. 6

5.1 Data

• The experiments use the IGLU Multi-Turn Dataset stepwise, with the latest Architect instruction and prior language context as input and block-placement sentences as output.The dialogue-only setup differs from the general IGLU task, so the authors establish separate baselines and metrics.Source: §5.1 · p. 6

5.2 Evaluation Metrics

• Evaluation combines IGLU Reward, Euclidean-squared Distance, number of Blocks Placed, and Help Followed, with mean prioritized over standard deviation.Reward measures invariant grid intersection, distance measures proximity to target blocks with a block-count adjustment, and Help Followed measures correct restrictive-region placement.Source: §5.2 · pp. 6–7

5.3 Help Feedback

• All four natural-language help conditions improve mean distance and mean reward over the BART baseline, and models follow help more often than chance.Mistake help raises average reward by approximately 25%, while standard deviation worsens in some cases because of outliers.Source: Table 1 · p. 7, §5.3 · p. 7

5.4 Self-Generated Help and Clarification ?’s

• Self-generated help achieves high accuracy for restrictive, corrective, and mistake-based guidance, but length-based help struggles because block counts are difficult to predict.This limitation in length-help prediction corresponds to weaker downstream performance for that help type.Source: §5.4 · p. 7, Table 2 · p. 7
• At test time, self-generated help improves performance in every setting except length help, with corrective help producing the strongest results and outperforming its accurately provided counterpart.The authors hypothesize that incorrect self-generated help often has little effect when the initial prediction is already wrong, while correct help narrows borderline predictions.Source: §5.4 · pp. 7–8
• The confusion-based clarification system improves over baselines, but performs worse than some self-generated-help models because it does not always identify when help is needed.The system supplies accurate help after selecting a clarification question through self-generated help.Source: §5.4 · p. 8, Table 3 · p. 8

Restrictive Help – Number of Regions

• An ablation over restrictive-help region counts selects eight regions as the configuration used for subsequent evaluation.The choice is based on test-set results reported in Table 4.Source: §6 · p. 8

Handling Inaccurate Help

• Self-generated help can improve performance even when its feedback is inaccurate, indicating that the model can adapt rather than rely solely on every help signal.The discussion links this behavior to the self-generated-help results in Tables 2 and 3.Source: §6 · p. 8

Varying Help Utterances

• LLMs can identify the regions expressed by varied restrictive-help utterances, supporting the approach’s ability to process more human-like language variation.The varied utterances are generated by LLMs and mapped back to regions before being supplied to the models.Source: §6 · p. 8

Improving Mean, but sometimes worsening Standard Deviation

• Mean performance consistently improves, but standard deviation sometimes worsens because a weak baseline fails on instructions that accurate help can further confuse.The authors attribute these cases to outliers and expect a stronger baseline to yield more consistent results.Source: §6 · p. 8

7 Conclusion and Future Work

• The proposed framework enables four forms of high-level help, autonomous confusion detection, and clarification questions in an interactive IGLU-style agent.Experiments report performance improvements across these interactive settings.Source: §7 · p. 9
• Future work will improve clarification questions, add help types, and generalize the framework beyond tasks requiring 2D or 3D navigation.The authors identify broader-domain application as an open direction rather than an evaluated result.Source: §7 · p. 9
• The authors propose adapting help concepts to domains such as summarization, where restrictive help could focus reading on relevant text and corrective help could identify missed topics.They also view the approach as directly applicable to navigation-based tasks such as robotics.Source: §7 · p. 9

8.1 Limitations

• The framework was trained and evaluated on IGLU, and its generalization to other instruction-following tasks remains untested.The authors present broader applicability as a hypothesis and future-work direction.Source: §8.1 · p. 9
• Scaling to larger datasets would likely require more compute and could affect performance and training time.Training used a single 12 GB Titan X GPU; hyperparameter search took a week.Source: §8.1 · p. 9

8.2 Ethics

• Malicious users could provide incorrect help, although the model can adapt because it also uses knowledge learned from training data.The authors identify malicious feedback as an ongoing area of study.Source: §8.2 · pp. 9–10

A Additional Related Works

• Prior work studies human-AI interaction, feedback, active learning, self-correction, and language-model-generated feedback across several domains.The paper positions its help framework alongside these approaches and suggests future combination with reinforcement learning.Source: A Additional Related Works · p. 12

B Help Details

• Help utterances are generated from gold or model-predicted coordinates and inserted into predefined slots for each help type.The appendix specifies how these slots are populated from task-relevant coordinates.Source: B Help Details · p. 12

B.1 Restrictive

• Restrictive help maps coordinates to named spatial regions, with center and outer-grid subdivisions producing eight region descriptions.Examples distinguish locations such as “upper left not in the center” from “upper left in the center.”Source: §B.1 · pp. 12–13

B.2 Length-Based / B.3 Corrective / B.4 Mistake-Based

• Length-based, corrective, and mistake-based help respectively specify block count, directional adjustment, and incorrectly placed block count.Each form is produced by filling a template with a number or one of four directions.Source: §B.2 · p. 13, §B.3 · p. 13, §B.4 · p. 13

C Implementation Details

• The models use PyTorch, Transformers, and Facebook BART-Base for Transformer language-model components.The implementation uses the Adam optimizer for training.Source: C Implementation Details · p. 13
• End-to-end models use learning rate 1e-4, while self-generated models use 1e-6 and an unfurnished classification layer.All models were trained on a 12 GB TITAN XP GPU.Source: C Implementation Details · p. 13
• Training the self-generated model took approximately 5 hours, whereas training the end-to-end models took anywhere from 1–2 days, using a 12GB TITAN XP GPU card.Source: C Implementation Details · p. 13

D Dataset Details

• The IGLU MultiTurn Dataset provides step-level examples whose inputs combine the latest Architect instruction with prior dialogue context.The model outputs a sentence describing where blocks should be placed, if any.Source: D Dataset Details · p. 13
• The dataset contains 8,736 training, 11,283 validation, and 1,238 test samples, with identical test sets across settings.Confusion and clarification models use half of the training/development data to learn self-generated help.Source: D Dataset Details · p. 13

E Ablation Study for Restrictive Help

• Eight regions provide the best restrictive-help performance in the smaller ablation dataset, motivating their use in the main experiments.The ablation compares different region counts.Source: E Ablation Study for Restrictive Help · p. 13

F Discussion Cont.: Varying Help Utterances

• ChatGPT-generated rewrites provide varied restrictive-help language, which is manually filtered for duplicates, validity, and conflicts.The rewrites simulate the linguistic variety expected from human helpers.Source: F Discussion Cont.: Varying Help… · pp. 13–14, Table 5 · p. 14
• ChatGPT achieves high accuracy when classifying varied help utterances into their intended regions, but end-to-end task performance is not evaluated.The authors therefore leave direct integration into the IGLU task model for future work.Source: F Discussion Cont.: Varying Help… · p. 14, Table 6 · p. 14
• The proposed pipeline uses ChatGPT to identify a region from natural-language help, then converts that prediction into a slot-filled utterance for the IGLU model.The authors hypothesize this can handle substantial language variation in human help.Source: F Discussion Cont.: Varying Help… · p. 14

G Real World Application of Help

• Each help type has a finite option set, allowing a few-shot classifier to identify both the help category and its specific option.Examples include eight regions, a maximum of eight blocks, four directions, and up to eight incorrect blocks.Source: G Real World Application of Help · pp. 14–15
• Figures illustrate ChatGPT rewriting help and classifying ambiguous region descriptions using centered few-shot examples that generalize to other regions.Repeated rewrites are discarded and generated outputs are manually inspected.Source: Figure 4 · p. 15, Figure 5 · p. 15
• The framework is demonstrated only in a single-step dialogue IGLU setup, though the authors suggest integrating help into reinforcement-learning agents.In that setting, a language-model component could provide help as an additional input.Source: G Real World Application of Help · p. 15

• Mean distance improves from 12.64 to 5.10, while STD distance improves from 51.73 to 9.10 for the best model over the baseline.These results are reported for Table 2, row A3.Source: H Discussion: Model Inconsistencies · p. 16
• The authors attribute inconsistent standard-deviation results partly to a weak baseline and could not test a stronger language model because of limited compute.They leave evaluation with a stronger baseline for future work.Source: H Discussion: Model Inconsistencies · p. 16
• Help usually improves mean performance, but can worsen standard deviation when the baseline fails to understand the initial instruction.In such cases, help may confuse the model and make its prediction worse.Source: H Discussion: Model Inconsistencies · p. 16
• Self-generated corrective help performs better than 100% accurate corrective help because it reinforces solvable predictions without dramatically changing unsolvable ones.Accurate help can instead cause large corrective shifts that confuse the model and worsen results.Source: H Discussion: Model Inconsistencies · p. 16
• The interaction protocols improve models overall, although their gains are constrained by cases where the baseline prediction is already far from correct.Help assists when the model can understand the initial example, but may hurt outlier cases.Source: H Discussion: Model Inconsistencies · p. 16