Papers with -shot prompts
Presentation Slide Translation and Layout Error Correction by LLMs (2026.acl-srw)
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| Challenge: | Existing translation tools suffer from layout errors due to text expansion during translation . a new approach to translating Japanese slides into English is proposed to overcome this issue . |
| Approach: | They propose a framework to translate Japanese slides into English and correct layout errors by using multimodal LLMs with slide images and XML structures. |
| Outcome: | The proposed method outperforms baselines and achieves 4.1% layout error rate and over 80% success rate. |
Generating Vehicular Icon Descriptions and Indications Using Large Vision-Language Models (2024.emnlp-industry)
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James Fletcher, Nicholas Dehnen, Seyed Nima Tayarani Bathaie, Aijun An, Heidar Davoudi, Ron DiCarlantonio, Gary Farmaner
| Challenge: | Existing image description systems are trained mainly on natural images, whereas icon images are drawings. |
| Approach: | They propose to use a dataset to generate both visual and functional icon descriptions based on the icon image and its context information in the car manual. |
| Outcome: | The proposed model performs well on the dashboard icon description task while the third model perform poorly. |
Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles (2025.acl-long)
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| Challenge: | Existing studies neglect to measure the generalization of their methods to other prompt styles and different sizes of LLMs. |
| Approach: | They propose a framework that trains an auxiliary model for confidence estimation that aggregates responses from multiple LLMs to capture inter-model agreement. |
| Outcome: | The proposed framework integrates response agreement and focal loss with binary cross-entropy to improve calibration from baselines. |
RoQLlama: A Lightweight Romanian Adapted Language Model (2024.findings-emnlp)
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| Challenge: | Currently, open-source large language models are limited to tasks involving the English language. |
| Approach: | They propose to use QLoRA to train a Romanian-adapted LLM with 7 billion parameters and quantized to 4 bits to improve model's performance. |
| Outcome: | The proposed model outperforms the other LLMs on four out of the seven tasks investigated using zero-shot prompting. |
Exploiting the Shadows: Unveiling Privacy Leaks through Lower-Ranked Tokens in Large Language Models (2025.acl-long)
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| Challenge: | Large language models face vulnerabilities related to the extraction of sensitive information. |
| Approach: | They propose a method to exploit the model's lower-ranked output tokens to extract private information from retrieved documents or training knowledge. |
| Outcome: | The proposed method is effective in both the agentic application privacy extraction setting and the direct training data extraction. |
Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction. |
| Approach: | They propose to use a test suite to evaluate the impact of shortcuts on LLMs' performance. |
| Outcome: | The proposed test suite incorporates six shortcut types, five evaluation metrics, and four prompting strategies. |
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have achieved great success in various NLP tasks, but the vast model parameters pose challenges in downstream fine-tuning. |
| Approach: | They propose a task-agnostic prompting strategy that analyzes each dialogue utterance before task execution to enhance LLMs' comprehension in multi-turn dialogues. |
| Outcome: | The proposed strategy outperforms other zero-shot prompts and matches or exceeds efficacy of few-shot ones. |
Soft Head Selection for Injecting ICL-Derived Task Embeddings (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are commonly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT) or in-context learning (ICL). |
| Approach: | They propose a gradient-based method that derives task-specific embeddings from activations using few-shot prompts and injects them during inference. |
| Outcome: | The proposed method outperforms existing methods on open-ended generation, reasoning, and natural language understanding tasks while using fewer trainable parameters. |