Papers with in-context learning capabilities
On the effective transfer of knowledge from English to Hindi Wikipedia (2025.coling-industry)
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| Challenge: | Existing studies show that in low-resource languages, Wikipedia articles on the same topic differ significantly due to cultural relevance and the varied expertise of contributors. |
| Approach: | They propose a lightweight framework to enhance knowledge equity between English and Hindi Wikipedia sections by extracting relevant information from external resources readily available. |
| Outcome: | The proposed framework enhances Hindi Wikipedia articles by 65% and 62% based on automatic and human judgment-based evaluations. |
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
| Challenge: | Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. |
| Approach: | They propose to use schema descriptions to facilitate zero-shot transfer to new domains . they argue that general purpose language models lack the ability to replace specialized systems . |
| Outcome: | The proposed method achieves state-of-the-art in zero-shot DST with in-context learning capabilities. |
Train a Unified Multimodal Data Quality Classifier with Synthetic Data (2025.findings-emnlp)
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Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard, Ritesh Sarkhel, Sanket Lokegaonkar, Jingbo Shang, Xifeng Yan, Nasser Zalmout, Xian Li
| Challenge: | Multimodal Large Language Models are pre-trained on image-text caption data and interleaved document data. |
| Approach: | They propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to filter image-text caption and interleaved data. |
| Outcome: | The proposed method enables efficient creation of sample-score pairs for caption and interleaved data to train UniFilter. |
Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel at generating code for high-resource programming languages (HRPLs) however, they struggle significantly with low-resourced programming languages such as D, exacerbating the digital divide. |
| Approach: | They propose a method to generate LRPL data using LLM's general knowledge, HRPL proficiency, and in-context learning capabilities. |
| Outcome: | The proposed method improves on R, D, Racket, and Bash, while maintaining the same quality. |
Demystifying Small Language Models for Edge Deployment (2025.acl-long)
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Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Wei Liu, Jian Luan, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu
| Challenge: | Small language models (SLMs) are a promising solution for resource-constrained devices such as smartphones and the Web of Things. |
| Approach: | They propose to use SLMs to build and optimize a set of small language models that are publicly accessible. |
| Outcome: | The proposed models outperform 7B models in general tasks, while their in-context learning capabilities remain limited and their efficiency has significant optimization potential. |
Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)
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| Challenge: | Existing attempts to apply large language models to BioEL have revealed difficulties . |
| Approach: | They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities . |
| Outcome: | Extensive experiments show that the framework outperforms existing LLMs. |