Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning (2024.emnlp-main)
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| Challenge: | In-Context-learning and few-shot prompting are viable methods for compositional output generation but they are sensitive to the choice of support examples. |
| Approach: | They propose a method which generates supports and targets current state of the world and then uses them in-context-learning to solve a query. |
| Outcome: | The proposed agent improves performance on a previously unsolved compositional generalization test without loss of performance in other areas. |
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| Challenge: | In-context learning has shown great success in i.i.d semantic parsing splits . however, in compositional generalization, selecting similar demonstrations is insufficient . |
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| Challenge: | Existing methods for combining language models with knowledge graphs struggle with generalization to sequences of unseen lengths and novel combinations of seen base components. |
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| Challenge: | Recent research shows that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. |
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Skills-in-Context: Unlocking Compositionality in Large Language Models (2024.findings-emnlp)
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| Challenge: | eliciting compositional generalization capabilities in large language models is challenging for advanced LLMs because they lack foundational skills and compositional examples in the same prompt context. |
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| Challenge: | Generalization of models to out-of-distribution data has sparked substantial interest . compositional generalization is the ability to systematically generalize to test examples composed of components seen during training . |
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Active Example Selection for In-Context Learning (2022.emnlp-main)
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| Challenge: | In-context learning performance is unstable across samples of examples, suggesting the idiosyncrasies of how language models acquire information. |
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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. |
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