Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in various NLP tasks, including semantic parsing, which translates natural language into formal code representations. |
| Approach: | They propose a semantic captioning task to repurpose semantic parsing datasets for semantic captions. |
| Outcome: | The proposed model outperforms random selection and other methods by 39% on BLEU score. |
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| Challenge: | In-context learning is an attractive approach for semantic parsing, but learning to parse to rare domain-specific languages from a few demonstrations is challenging. |
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
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| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
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| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
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| Challenge: | Existing work focuses on English datasets, and it is unclear whether large language models can serve as competitive semantic parsers for other languages. |
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| Challenge: | Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models. |
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C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)
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| Challenge: | Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples. |
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Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)
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Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev
| Challenge: | In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory. |
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Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding (2025.acl-short)
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| Challenge: | Prior research has found that large language models overlook input-label mapping information in ICL, relying more on their pre-trained knowledge. |
| Approach: | They propose a novel method that contrasts input-label mappings between positive and negative in-context examples to improve model performance. |
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DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph (2025.acl-long)
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| Challenge: | Existing methods for Text-to-SQL show little improvement compared to random selections . Existing approaches rely on intrinsic capabilities of hyper-scaled LLMs, not useful demonstrations. |
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