37 citations · 83 across the 13 of their papers we have counts for
12 papers · 1 filter
EffComp: Efficient Prompt Compression via Hybrid Reinforcement & Supervised Learning
Romtham Tungsununtham, Piyawat Lertvittayakumjorn, Atiwong Suchato +1
Prompt compression is aimed at reducing input prompt lengths to enable cheaper, faster, and potentially higher-quality LLM predictions. However, existing prompt compression methods…
What Limits Us? Analyzing Self-Reported Limitations in NLP Research
Tawan Thaepprasit, Peeranuth Kehasukcharoen, Ding Wang +3
Since late 2022, a Limitations section has become mandatory at many top-tier NLP conferences. The growing number of accepted papers at these venues has resulted in a vast corpus of…
Towards Geo-Culturally Grounded LLM Generations
Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran +2
Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and searc…
Label-Aware Automatic Verbalizer for Few-Shot Text Classification
Thanakorn Thaminkaew, Piyawat Lertvittayakumjorn, Peerapon Vateekul
Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language mode…
Towards Explainable Evaluation Metrics for Machine Translation
Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva +3
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics for machine translation (for example, COMET or BERTScore) are based on black-box large langua…
Towards Explainable Evaluation Metrics for Natural Language Generation
Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva +3
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such as BERTScore or MoverScore) are based on black-box language models such as BERT or XLM-…
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results
Marina Fomicheva, Piyawat Lertvittayakumjorn, Wei Zhao +2
In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentenc…
Explanation-Based Human Debugging of NLP Models: A Survey
Piyawat Lertvittayakumjorn, Francesca Toni
Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if…
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Piyawat Lertvittayakumjorn, Leshem Choshen, Eyal Shnarch +1
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a…
FIND: Human-in-the-Loop Debugging Deep Text Classifiers
Piyawat Lertvittayakumjorn, Lucia Specia, Francesca Toni
Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text…
Human-grounded Evaluations of Explanation Methods for Text Classification
Piyawat Lertvittayakumjorn, Francesca Toni
Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and huma…
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Jingqing Zhang, Piyawat Lertvittayakumjorn, Yike Guo
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of cla…