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BERTs Are Generative In-Context Learners
- srameshc 2y agoAs someone who has very limited understanding but tried to use BERT for classification, is BERT still relavant when compared to LLMs ? Asking because I hardly see any mention of BERTs anymore.
- mynegation 2y agoFor many specialized tasks you can run BERTs (and simpler models in general) at scale, with lower latency, at lesser cost, with similar or even better results.
- galeos 2y agoMy understanding is that BERT can still outperform LLMs for sentiment classification?
- jsemrau 2y agoTo my understanding yes. But I never found a good use-case for sentiment classification.
- ta8645 2y agoIt seems to be used by Youtube for comment censoring / shadow-banning.
- jsemrau 2y agoThat might make sense.
- antononcube 2y agoI used sentiment analysis a few times in recommender systems (for digital media consumption.) Also for analyzing Trump's tweets (from 2016): https://mathematicaforprediction.wordpress.com/2016/11/21/text-analysis-of-trump-tweets/ https://mathematicaforprediction.wordpress.com/2016/11/21/te...
- hackernewds 2y agoReally cool investigation. Would you mind sharing the BERT specific sentiment detection?
- antononcube 2y agoThanks! See the Mathematica code in this Markdown file: https://github.com/antononcube/MathematicaVsR/blob/master/Projects/TextAnalysisOfTrumpTweets/Mathematica/Text-analysis-of-Trump-tweets.md https://github.com/antononcube/MathematicaVsR/blob/master/Pr...
- hackernewds 2y agoSafety
- beoberha 2y agoDepends what you’re trying to do. I’m writing a personal assistant app (speech to text) and want to classify the user input according to the current actions I support (or don’t). The flagship LLMs are pretty great at it if you include the classes in the prompt and they will spit out structured output every time. But, man, they are expensive and there’s the privacy aspect I’d prefer to adhere to. I’ve only got 24 GB of RAM, so I can’t run too many fancy local models and things like llama3.1:8b don’t classify very well. So I’m trying BERT models out :)
- Tostino 2y agoTry some of the Quen models. They have some that are slightly larger than 8b that will fit on your 24gb quite nicely. They have been amazing so far.
- osanseviero 2y agoYes, they are still used - Encoder based models have much faster inference (are auto-regressive) and are smaller. They are great for applications where speed and efficiency are key. - Most embedding models are BERT-based (see MTEB leaderboard). So widely used for retrieval. - They are also used to filter data for pre-training decoder models. The Llama 3 authors used a quality classifier (DistilRoberta) to generate quality scores for documents. Something similar is done for FineWeb Edu
- itchyjunk 2y agoWait, I thought GPT's were autoregressive and encoder only like BERT used masked tokens? You're saying BERT is auto-regressive or am I misunderstanding?
- woadwarrior01 2y agoYou're right. Encoder only models like BERT aren't auto-regressive and are trained with the MLM objective. Decoder only (GPT) and encoder-decoder (T5) models are auto-regressive and are trained with the CLM and sometimes the PrefixLM objectives.
- ipsum2 2y agoYou can mask out the tokens at the end, so its technically autoregressive.
- spmurrayzzz 2y agoThey're still very useful on their own. But even more broadly, you can often use them in tandem with LLMs. A good example could be a classifier that's used as a "router" of sorts; could be for selecting a prompt template, directing to a specific model, or loading a LoRA or soft prompt vector to be used at inference-time.
- deepsquirrelnet 2y agoThey’ve drowned in the LLM noise, but they’re definitely still relevant. - Generative model outputs are not always desirable, and often even undesirable - BERT models are smaller and can run with lower latency and serve larger batches with lower vram requirements - BERT models have bidirectional attention, which can improve performance in many applications LLMs are “cheap” in the sense that they work well generically, without requiring fine tuning. Where they overlap with BERT models is mostly that they may work better in low training data environments due to better generalization capabilities. But mostly companies like them because they don’t “require” ML engineers or data scientists on staff. For the lack of care given to evaluation that I see around LLM apps, I suspect that’s going to prove to be a faulty premise.
- antononcube 2y ago> - BERT models are smaller and can run with lower latency and serve larger batches with lower vram requirements The most recent version of Wolfram Language (aka Mathematica) uses by default BERT models for embedding. (Say, for this function: https://reference.wolfram.com/language/ref/CreateSemanticSearchIndex.html https://reference.wolfram.com/language/ref/CreateSemanticSea... .)
- a_bonobo 2y agoStill relevant in DNA space! https://arxiv.org/abs/2402.08777 https://arxiv.org/abs/2402.08777
- hackernewds 2y agoBERT is favored for generating embeddings and bag of words strategies for clustering
- gunalx 2y agoI think berts are still used for stuff like embeddings (at least the best generic embedding model in my language is bert based), when you want to get a model that has basic semantics but needs to be fast.
- patelajay285 2y agoWe found the same result a few years ago in our ICLR paper: https://arxiv.org/pdf/2209.14500 https://arxiv.org/pdf/2209.14500 We found Google's T5 models which were released in 2019, pre-GPT-3, were "secretly" capable of in-context learning with a simple inference technique. Given they use a bidirectional MLM (Masked Language Modeling) objective, it wasn't obvious how to do it, but MLM objectives are known to produce better language representations than causal (next token prediction) objectives. We were able to outperform much larger sized GPT-3 models or get very close to their performance with far smaller T5 models.
- cscurmudgeon 2y agoAre there any intrinsic dis/advantages of bidirectional models over causal models for in-context learning? It seems that unidirectional model just have been explored and worked on more.
- patelajay285 2y agoWhen you train bidirectionally only, you don't get a generative model, that would be the downside. However, you can train on a mixture of causal and bidirectional objectives as some LLM pre-training has done. As far as I am aware, there are no downsides of that, but it is not more common simply because the standard practice has been to train causal only and there just isn't enough funding/attention to go into experimenting on every axis of pre-training (which can be very expensive).
- jsenn 2y agoThe "embarrassingly simple inference technique" is to put a bunch of [MASK] tokens at the end of the prompt. I'm having trouble understanding whether this paper is saying anything new. The original BERT paper already compared it favourably to causal models including GPT. Was there any doubt that BERT-style models could be in-context learners? From what I gather as a non-expert, the problem with BERT is scaling/training efficiency: GPT gets C-1 training examples out of a training input of length C, but BERT only gets 0.15*C examples. Indeed, the author points out that DeBERTa required 3x more compute than GPT-3 to achieve the level of performance reported, which makes sense.
- Ldorigo 2y agoDo we a know whether the current SOTA foundation models (Gemini, gpt4o, Claude, etc) are actually all GPT-based (as in, causal models)?
- dartos 2y agoGPT-based isn’t really a thing outside of openai (it’s just the commercial name for their models) But I believe we’re confident that all major models are causal transformer models right now. No reason to believe otherwise. If one of them was doing something different, they’d let us know in order to stand out.
- Tostino 2y agoNo, they didn't get to co-opt that word.
- dartos 2y agoThey literally invented the term GPT… “Transformer” is the name of the algorithm behind popular LLMs. GPT is the name that openai gave to their models early on.
- falcor84 2y agoWhat does having invented a term have to do with this? The Otis company invented the term escalator, and even had a trademark on it for a while, but does it mean that you'd only call one an escalator if it was made by them?
- collingreen 2y agoThat's literally what the trademark means. At some point things become so dominant and generic a trademark is no longer successfully enforceable and you get escalators, bandaids, linoleum, taser, gasoline, etc.
- dartos 2y ago