Challenge: a new direction for semantic parsing that models explanations to demonstrations is proposed . bottom-up approach to generating logical forms is complicated in domains with rich composition .
Approach: They propose a new direction for semantic parsing that models explanations in a context . they use inverse semantics to reason backwards from observed demonstrations .
Outcome: The proposed approach shows better task completion rates than a baseline method . it is competitive with exploration-and-demonstration based methods, but requires no exploration of environment .

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Learning API Functionality from In-Context Demonstrations for Tool-based Agents (2025.findings-emnlp)

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Challenge: Documentation is often missing, outdated, privatized, or inconsistent in tool-based agents.
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The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis (2024.findings-acl)

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Challenge: In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without updating the model parameters.
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LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)

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Challenge: In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE).
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Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)

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Challenge: In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge.
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Prompting Contrastive Explanations for Commonsense Reasoning Tasks (2021.findings-acl)

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Challenge: Large pretrained language models (PLMs) can achieve near-human performance on commonsense reasoning tasks, but provide little human-interpretable evidence of the underlying reasoning they use.
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CLIX: Cross-Lingual Explanations of Idiomatic Expressions (2025.findings-acl)

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Challenge: Existing definition generation systems are difficult to use in second language learning due to the presence of unfamiliar words and grammar.
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An Imitation Game for Learning Semantic Parsers from User Interaction (2020.emnlp-main)

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Challenge: Existing methods for learning semantic parsers are expensive and tedious . despite the widespread applications, bootstrapping and fine-tuning is tedious a task .
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Negation, Coordination, and Quantifiers in Contextualized Language Models (2022.coling-1)

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Challenge: Recent work has focused on specific tasks and on the learning outcome.
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Counterfactual Explanations for Natural Language Interfaces (2022.acl-short)

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Challenge: Semantic parsing is a promising technique for enabling natural language interfaces, but human language can encode concepts that do not exist in the underlying system or are encoded using different language.
Approach: They propose a novel approach for generating explanations of a natural language interface based on semantic parsing by providing a user with an utterance and a demonstration of their desired goal.
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Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation (2023.acl-short)

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Challenge: Existing work only concatenates answered examples as demonstrations to prompt template without any additional operation, neglecting the prompt-demonstration dependencies.
Approach: They propose to concatenate answered examples as demonstrations to prompt template without any additional operation, neglecting the prompt-demonstration dependencies.
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