Papers with in-context learning strategies
Assessing In-context Learning and Fine-tuning for Topic Classification of German Web Data (2024.acl-srw)
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| Challenge: | Using a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. |
| Approach: | They propose to use annotated data to train a binary classification task to detect topic-related content in a scraped database of webpages. |
| Outcome: | The proposed model detects content related to three German policies in a scraped database of scrapes of webpages using a few hundred annotated data points per topic. |
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning (2023.acl-short)
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| Challenge: | Negation is a ubiquitous but complex linguistic phenomenon that poses a significant challenge for NLP systems. |
| Approach: | They propose a benchmark that measures how well models handle natural language negation . they extend ScoNe-NLI to embed negation reasoning in short narratives . |
| Outcome: | The proposed model can reason about negation, but struggles to do so on NLI examples outside of its core pretraining regime. |
Exploring Backward Reasoning in Large Language Models (2025.findings-naacl)
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| Challenge: | Multi-step reasoning through in-context learning strategies have been extensively explored, highlighting the abilities of Large Language Models (LLMs) to solve problems in a step-wise manner. |
| Approach: | They propose to use Large Language Models to generate answers from step-by-step reasoning by re-constructing the original question that led to the final answer. |
| Outcome: | The proposed models show that they are able to reason about the conclusion and reconstruct the original question that led to the final answer. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
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| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |