Papers with PHD
PHD: Pixel-Based Language Modeling of Historical Documents (2023.emnlp-main)
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| Challenge: | Recent years have seen a boom in efforts to digitise historical documents in numerous languages and sources, leading to a transformation in the way historians work. |
| Approach: | They propose a method for generating synthetic scans to resemble real historical documents by pre-training a model to reconstruct masked patches instead of predicting token distributions. |
| Outcome: | The proposed model can reconstruct masked patches and understand language well. |
A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are capable of working with humans in real-world scenarios, but they are prone to generate hallucinations and misinformation when deployed for mission-critical tasks. |
| Approach: | They propose a self-check approach to detect factual errors in a zero-resource fashion by using reverse validation to generate a hallucination detection benchmark. |
| Outcome: | The proposed method outperforms baseline methods while costing fewer tokens and less time. |
A Practical Examination of AI-Generated Text Detectors for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing methods to detect large language models are prone to misuse, such as generating fake news articles, facilitating academic plagiarism or spamming. |
| Approach: | They evaluate several popular detectors to evaluate their effectiveness against a range of domains, datasets, and models. |
| Outcome: | The proposed methods perform poorly in certain settings, with TPR@.01 as low as 0%. |