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LLMs are useful for things like predicting/generating text, and summarizing text. They are not useful if you want to do other NLP tasks that include things like
by rhdunn 3y ago
LLMs are useful for things like predicting/generating text, and summarizing text. They are not useful if you want to do other NLP tasks that include things like:
1. Identifying (and highlighting/extracting) the language that spans of text are in within a different language (e.g. a French phrase in English).
2. Text search and highlighting, where you need to do things like performing word stemming or lemmatization on the search input, find matching documents, and highlight spans.
Also, LLMs have a different tokenization model, so you have tokens like "don"/"'t!" -- this makes them difficult to work with in downstream NLP applications where having words, numbers, punctuation, and symbols as specific tokens are more useful.
NLP is not a solved task, as things like part of speech classification (identifying nouns, adjectives, etc.) are not 100% accurate, and tend to have a lower sentence accuracy compared to word accuracy.
- yorwba 3y ago1. Prompt: """ The following English text contains several French phrases. List all of them. Text: Gabonese President Ali Bongo Odimba was deposed in a coup d'etat spearheaded by his father's former aide-de-camp Brice Oligui Nguema, shortly after the announcement that Bongo had won the 2023 election. List of French phrases from the text: - """ Response: """ coup d'etat - aide-de-camp """ 2. Prompt: """ Turn all words in the following text into their lemma form. Text: Gabonese President Ali Bongo Odimba was deposed in a coup d'etat spearheaded by his father's former aide-de-camp Brice Oligui Nguema, shortly after the announcement that Bongo had won the 2023 election. Lemmatized text: """ Response: """ Gabonese President Ali Bongo Odimba be depose in a coup d'etat spearhead by his father's former aide-de-camp Brice Oligui Nguema, shortly after the announcement that Bongo have win the 2023 election. """ Maybe too slow or too inaccurate or too expensive compared to bespoke solutions, but certainly not completely useless. If you want a different tokenization, just turn the LLM response back into text and retokenize it.
- croes 3y agoLLMs is non deterministic, so tomorrow the same prompt can give you a totally different result. That's what renders it nearly useless for such tasks, because you always have to check everything.
- yorwba 3y agoThere's nothing about LLMs that is inherently nondeterministic. Sure, if you're using some API you have no control over, anything could happen. But if you run it on your own hardware, you can make it as deterministic as any classic NLP approach. And whether you always have to check everything is a separate question from nondeterminism. You could have a deterministic heuristic that is often wrong in a domain where mistakes are fatal, or you could have a nondeterministic model that is almost always correct for a task where errors cost next to nothing.
- squeaky-clean 3y agoChatGPT 4 is inherently non-deterministic. Even across the same exact model version with temperature set to 0. https://ai.plainenglish.io/gpt-4-is-non-deterministic-af202847529c https://ai.plainenglish.io/gpt-4-is-non-deterministic-af2028...
- yorwba 3y agoThat's what I meant by "if you're using some API you have no control over, anything could happen." OpenAI could offer access to a fully deterministic version at a higher price point, but they choose not to and there's nothing you can do about that. FYI, your source is medium-walled blog spam of https://152334h.github.io/blog/non-determinism-in-gpt-4/ https://152334h.github.io/blog/non-determinism-in-gpt-4/
- squeaky-clean 3y agoIf you'd read the source you linked you'd see that no, the system is inherently non-deterministic. They cannot sell you a deterministic version of GPT-4. There is no such thing. They could sell you a deterministic version of some other inferior model.
- yorwba 3y agoFrom the source, which I read: As a consequence, the model is no longer deterministic at the sequence-level, but only at the batch-level They could sell you a version where you shove in a whole batch, pay $batch_size as much, and always get the same result for the same batch.
- billythemaniam 3y agoI agree LLMs alone aren't good at search but their embeddings replace the need for stemming, manual synonym lists, etc in most cases. LLMs can also be used for query understanding which can improve the keywords submitted to the engine and extracting the best snippet for a highlight. LLMs + search are better than either alone. However LLMs still have an inference performance/cost issue which may make them unsuitable for some search use cases.