Challenge: RUSS is a task and dataset to ground natural language instructions on the web to perform previously unseen tasks.
Approach: They build a task and dataset to ground AI agents from open-domain, step-by-step instructions on the web.
Outcome: The proposed model outperforms existing models that map instructions to actions without WebLang.

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Improving Natural Language Interaction with Robots Using Advice (N19-1)

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Challenge: Recent studies focus on learning models for physically grounded language understanding tasks such as the blocks world domain.
Approach: They propose a protocol for including advice, high-level observations about the task, which can help constrain the agent’s prediction.
Outcome: The proposed approach can be extended to include advice, high-level observations about the task, and reduce the effort involved in supplying the advice.
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (2021.naacl-main)

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Challenge: Empirical results show that today’s language models struggle at TuringAdvice . language models are getting ever-larger, and are being trained on ever-increasing quantities of text .
Approach: They propose a task task that requires models to generate helpful advice in natural language.
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tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)

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Challenge: Existing systems for natural language understanding (NLU) are limited due to the inherent ambiguity and incompleteness inherent in natural language.
Approach: They propose a system to extract tasks from natural language instructions and map them to robots' established collection of skills.
Outcome: The proposed system outperforms baseline models in the training and evaluation of a dataset featuring complex instructions.
Mapping natural language commands to web elements (D18-1)

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Challenge: a dataset of over 50,000 natural language commands captures various phenomena, including functional references, relational reasoning, and visual reasoning.
Approach: They propose a task that requires the user to choose the correct element on a web page . they use a dataset of over 50,000 natural language commands to map these to web pages .
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LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)

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Challenge: a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist .
Approach: This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision.
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FLIN: A Flexible Natural Language Interface for Web Navigation (2021.naacl-main)

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Challenge: Existing semantic parsing and slot-filling techniques cannot adapt to many different websites without being constantly re-trained.
Approach: They propose a natural language interface for web navigation that maps user commands to concept-level actions rather than low-level UI actions.
Outcome: The proposed interface can adapt to new websites in a given domain.
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (2021.naacl-main)

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Challenge: Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages.
Approach: They propose to model teacher-learner dynamics through natural interactions occurring between users and search engines.
Outcome: The proposed model is better than non-grounded models on compositionality and zero-shot inference tasks.
Infusing Finetuning with Semantic Dependencies (2021.tacl-1)

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Challenge: Several diagnostics help to localize the benefits of our approach.
Approach: They apply convolutional graph encoders to integrate semantic parses into task-specific finetuning.
Outcome: The proposed approach yields benefits to natural language understanding (NLU) tasks in the GLUE benchmark.
Mapping Natural Language Instructions to Mobile UI Action Sequences (2020.acl-main)

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Challenge: a new problem of grounding natural language instructions to mobile UI actions is emerging . we use a Transformer to extract action phrase tuples from long-range natural language instruction .
Approach: They propose a dataset that pairs English instructions with actions performed by people on a mobile UI emulator.
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From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding (2025.acl-long)

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Challenge: a pursuit of diverse, complex, and large-scale instruction data is crucial for automatically aligning large language models . authors: methods that generate synthetic instructions at scale suffer from limited grounding sources . attributed grounding is a technique that can be used to align language models with human .
Approach: They synthesize 1 million instructions using attributed grounding and a bottom-up synthesis process that leverages web documents to generate a situation, then a meaningful instruction.
Outcome: The proposed framework achieves leading performance on benchmarks and scales with more web corpora.

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