13 citations · 29 across the 7 of their papers we have counts for
9 papers
Conversational Prompt Engineering
Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector +5
Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often tedious and tim…
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications
Alon Halfon, Shai Gretz, Ofir Arviv +6
Fine-tuning Large Language Models (LLMs) is an effective method to enhance their performance on downstream tasks. However, choosing the appropriate setting of tuning hyperparameter…
Zero-Shot Text Classification with Self-Training
Ariel Gera, Alon Halfon, Eyal Shnarch +3
Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference dataset…
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Eyal Shnarch, Alon Halfon, Ariel Gera +19
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML kn…
Cluster & Tune: Boost Cold Start Performance in Text Classification
Eyal Shnarch, Ariel Gera, Alon Halfon +4
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained model…
Corpus Wide Argument Mining -- a Working Solution
Liat Ein-Dor, Eyal Shnarch, Lena Dankin +10
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively sm…