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Based on the ROUGE-2 optimized alignments, generate sentence-based samples that can be used as training inputs for a paraphrasing model.
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* one friend work on more fine-grained and controllable paraphrase generation
* he wants to use automated metrics to assess the quality of generated paraphrases and filter low-quality ones
* he woul…
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Dear FABE Authors,
I hope this message finds you well. My name is Lily Logan, and I am currently a research assistant at the University of Oregon, working under PhD student Wencong You and Professo…
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**Describe the bug**
Tried to generate a 5000 words article with claude haiku and claude sonnet. Settings for the token:
```
conv_simulator_lm = ClaudeModel(model='claude-3-haiku-20240307', m…
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Labeling samples is time-consuming, manual and error-prone. The current process involves labeling elements using developer tools with the [FathomFox](https://github.com/mozilla/fathom-fox) extension. …
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# Toxic Speech Neutralization
The task of Toxic Speech Neutralization involves transforming a piece of speech identified as toxic (i.e., containing offensive, harmful, or abusive language) into a v…
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I am new to NLP and working on a personal project. Can you direct me to how I can use these models to generate similar sentences to an input sentence?
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NLG, the other end of NLP, is important in many fields where AI is being applied. Please include the latest NLG research as well as imo it would be very helpful.
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https://paperswithcode.com/task/text-augmentation
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When reading the ROME paper, I found this sample from CounterFact:
{
"case_id": 1067,
"pararel_idx": 21136,
"requested_rewrite": {
"prompt": "The headquarters of {} is in",
…