Papers by Douglas Eck
Automatic Detection of Generated Text is Easiest when Humans are Fooled (2020.acl-main)
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| Challenge: | Recent advances in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. |
| Approach: | They compare decoding methods with popular sampling-based decoding strategies . they show that multi-sentence excerpts can fool expert human raters over 30% of the time . |
| Outcome: | The proposed methods improve with longer excerpt length, but multi-sentence excerpts fool human raters over 30% of the time. |
Toward Better Storylines with Sentence-Level Language Models (2020.acl-main)
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| Challenge: | Rather than modeling fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. |
| Approach: | They propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. |
| Outcome: | The proposed model can focus on longer range dependencies, crucial for multi-sentence coherence. |
Deduplicating Training Data Makes Language Models Better (2022.acl-long)
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Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini
| Challenge: | Existing language modeling datasets contain near-duplicate examples and long repetitive substrings. |
| Approach: | They develop tools that allow us to deduplicate existing language modeling datasets . they found that over 1% of the unprompted output of language models is copied verbatim . |
| Outcome: | The proposed tools reduce train-test overlap, which affects over 4% of validation sets, and improve model accuracy. |