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Kind of .. depending on how sensitive you are to accuracy. Also also, we've had powerful summarization techniques for decades now (deep-learning, or otherwise)
by macspoofing 3y ago
Kind of .. depending on how sensitive you are to accuracy.
Also also, we've had powerful summarization techniques for decades now (deep-learning, or otherwise) - and they aren't susceptible to the hallucinations of present day Generative LLMs.
- simonw 3y agoWhich summarization models aren't susceptible to hallucination? Is there any good writing out there about this issue?
- netruk44 3y agoI found an 'old' (2016) reddit thread describing how auto tldr works, which doesn't suffer from hallucination (because it's only a statistical model that outputs original sentences from the text): https://old.reddit.com/r/askscience/comments/4s5b5q/how_exactly_does_a_autotldrbot_work/ https://old.reddit.com/r/askscience/comments/4s5b5q/how_exac...
- sebmellen 3y agohttps://huggingface.co/docs/transformers/model_doc/t5 https://huggingface.co/docs/transformers/model_doc/t5 is quite good for summarization!
- dontupvoteme 3y ago>we've had powerful summarization techniques for decades now (deep-learning, or otherwise) - and they aren't susceptible to the hallucinations of present day Generative LLMs. do you mean BERT, Seq2Seq and similar? My biggest takeaway from summarization tasks was that the benchmarks/evals seemed rather.. lacking.
- awkward 3y agoLike many use cases for LLMs, this seems like it would benefit a lazy student trying to avoid work. Rolling in some statements that are related but not mentioned in the input isn't a bad thing when you're studying for a test. However, for most use cases, falsely attributing statements is a terrible failure case for a summarizing tool.