Papers with prompt formulation
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction (2022.lrec-1)
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| Challenge: | Recent research shows that prompt-based learning improves performance on relation extraction tasks. |
| Approach: | They propose a prompt-based learning method that generates comprehensive prompts for biomedical relation extraction using a ChemProt dataset. |
| Outcome: | The proposed method improves fine-tuning on a biomedical relation extraction task with a cloze-test task and fewer training examples to make reasonable predictions. |
Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)
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| Challenge: | Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data. |
| Approach: | They propose a framework that facilitates information refinement through synergy between RMs and LLMs. |
| Outcome: | The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks. |
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora (2022.coling-1)
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| Challenge: | Existing models for textual emotion classification depend on domain and application scenario and need to be predefined . a natural language inference model with a flexible set of labels is difficult to develop . |
| Approach: | They propose to use the paradigm of zero-shot learning as a natural language inference task to generate a model with a flexible set of labels. |
| Outcome: | The proposed model is more robust across corpora than individual prompts and shows similar performance to the best prompt for a particular corpus. |
Around the World in 24 Hours: Probing LLM Knowledge of Time and Place (2025.acl-long)
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| Challenge: | Existing studies have tested language models' ability to reason over time and space in isolation or only in simple or artificial environments. |
| Approach: | They present a dataset of 320k prompts covering 289 cities in 217 countries and 37 time zones to evaluate their ability to jointly reason over time and space. |
| Outcome: | The proposed models perform well on reasoning tasks involving only temporal knowledge, but performance remains constrained on tasks that require connecting temporal and geographic information. |