| Challenge: | Pretrained language models (LMs) are the main interface for applying them to new tasks, but their large size makes them difficult to fine-tune with gradients for specific downstream tasks. |
| Approach: | They propose to use training data to form a decision tree based on prompt-LM calls, with each prompt determined by the outcomes of previous calls. |
| Outcome: | The proposed method improves accuracy over competing methods and is competitive with fine-tuning. |
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Fast Adaptation via Prompted Data: An Efficient Cross-Domain Fine-tuning Method for Large Language Models (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have been successful in a variety of natural language understanding tasks, but domain discrepancies between the downstream task and the pre-training corpora may have hindered LLMs to excel further in the vertical applications. |
| Approach: | They propose a Fast Adaptation method for LLMs via Prompted Data that integrates downstream text corpora, gold labels and external knowledge sources into a highly controllable prompt. |
| Outcome: | The proposed method bridges the gap between the downstream task and the pre-training corpora and integrates downstream text corpors, gold labels and external knowledge sources into a highly controllable prompt. |
Revisiting Automated Prompting: Are We Actually Doing Better? (2023.acl-short)
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| Challenge: | Recent work demonstrates that Large Language Models are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks. |
| Approach: | They revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. |
| Outcome: | The proposed approach outperforms manual prompting on six different downstream tasks and a larger range of K-shot learning settings. |
The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models (2025.findings-naacl)
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| Challenge: | Recent studies have focused on the inputoutput behavior of LMs, leaving the internal mechanisms behind ICL largely unexplored. |
| Approach: | They investigate how different prompting methods modify internal representations in pre-trained language models. |
| Outcome: | The proposed model can be programmed with natural language to perform a wide array of tasks without expensive fine-tuning. |
Reasoning for Translation: Comparative Analysis of Chain-of-Thought and Tree-of-Thought Prompting for LLM Translation (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) have been used for specialized tasks but their application to machine translation has received little attention. |
| Approach: | They evaluate reasoning-based prompting strategies across multiple language pairs and domains and measure their effect on translation quality. |
| Outcome: | The proposed prompting strategies outperform traditional prompting methods across language pairs and domains and achieve improvements of up to 6.4 BLs. |
Forest for the Trees: Overarching Prompting Evokes High-Level Reasoning in Large Language Models (2025.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have greatly propelled the progress of natural language process (NLP). |
| Approach: | They propose a deductive paradigm that decomposes the reasoning process and a prompting method that elicits high-level thinking of large language models (LLMs). |
| Outcome: | The proposed method improves ChatGPT and CoT by 19.0% and 3.1% on MMLU’s College Physics, 8.8% and 2.3% on GSM8k, and 10.3% and 2.5% on StrategyQA, respectively. |
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges (2025.acl-demo)
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Thilo Spinner, Rita Sevastjanova, Rebecca Kehlbeck, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Andreas Spitz, Mennatallah El-Assady
| Challenge: | generative language models have become popular, but comprehending their outputs remains challenging for NLP practitioners and linguistic experts. |
| Approach: | They propose to use a beam search tree to examine model outputs to provide information on runner-up candidates and their corresponding probabilities to address these challenges. |
| Outcome: | The proposed method validates existing results and offers additional insights. |
Prompterator: Iterate Efficiently towards More Effective Prompts (2023.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) use a process known as prompting to solve arbitrary language tasks. prompting is a non-trivial task that requires experimentation in order to arrive at a prompt that solves a specific task. |
| Approach: | They propose a tool that helps users iterate over different potential prompts and choose the best performing one based on human feedback. |
| Outcome: | The proposed tool is open source and easily extensible. |
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)
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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao
| Challenge: | Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. |
| Approach: | They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks. |
| Outcome: | The proposed method can optimize prompts for an LLM in downstream tasks. |
What Makes a Good Natural Language Prompt? (2025.acl-long)
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| Challenge: | Existing studies on prompt quality show imbalanced support across models and tasks, and research gaps. |
| Approach: | They propose a property- and human-centric framework for evaluating prompt quality . they propose comparing prompt quality to other factors such as adverbs and apverbs . |
| Outcome: | The proposed framework reveals imbalanced support across models and tasks and substantial research gaps. |
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2022.findings-acl)
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| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
| Approach: | They propose to fine tune masked language models with training examples and task descriptions to reduce prompt engineering by using null prompts. |
| Outcome: | The proposed prompts can be used to improve few-shot learning by finetuning only the bias terms while updating only 0.1% of the parameters. |