Submitted by Singularian2501 t3_10svwch in MachineLearning

Paper: https://arxiv.org/abs/2302.00923

Github: https://github.com/amazon-science/mm-cot

Twitter: https://paperswithcode.com/top-social

Abstract:

>Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies are mostly isolated in the language modality with LLMs, where LLMs are hard to deploy. To elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning. The key challenge is that those language models tend to generate hallucinated reasoning chains that mislead the answer inference. To mitigate the effect of such mistakes, we propose Multimodal-CoT that incorporates vision features in a decoupled training framework. The framework separates the rationale generation and answer inference into two stages. By incorporating the vision features in both stages, the model is able to generate effective rationales that contribute to answer inference. With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16% (75.17%->91.68%) on the ScienceQA benchmark and even surpasses human performance.

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Comments

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throwaway2676 t1_j74iilz wrote

Imo, chain-of-thought and program-of-thought reasoning will be the next major generation of progress for LLMs. Probably another year or two and we will be able to eliminate those goofy instances where the models confidently produce nonsense (well, mostly anyway).

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AiChip t1_j74ku5a wrote

Wow! This is huge! 1B parameters model beating 175 B parameters model…

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Lengador t1_j74ro7q wrote

That's the number in the headline, but if you look at the tables you can see their 223M parameter model beats the 175B parameter model significantly as well. That's 0.1% the size! Absolutely insane.

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zbyte64 t1_j74y5o9 wrote

What kind of hardware do I need to train this?

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ThirdMover t1_j760ojx wrote

I think it's going to be interesting if we manage to teach a model to actually have a notion of "factual" and "counterfactual" - right now every prompt is treated as equally valid, GPT3 doesn't have an "opinion" as to what is actually really true. I am not sure that is even possible with text (maybe with some sort of special marker token?) but multimodality might lead the way there.

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HunteronX t1_j761xqh wrote

The economics is getting there for these models to be big news...
The key features of this work seem to be:

  1. A multimodal embedding representation obtained by individual modality encoders (patch-level for images, token level for text), combined via attention.

  2. Generate rationales first, then infer answers from them, due to accuracy reduction on answers.
    (Not an expert: but is the greater % of hallucinated rationales in baseline case - no vision features - due to large 'context' needed for both rationale + answer, without those features?)

Seems that multimodal representations (language + n=? other modalities) may be important for introducing a loose physical grounding to avoid hallucinating plausible ideas/suggestions + efficient representation of the remaining ideas.

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dancingnightly t1_j76t0gh wrote

In theory training T5 alongiside the image embedding models they use (primarily DETR?) shouldn't take much more than a 3090 or Collab Pro GPU. You could train T5s on even consumer high end GPUs in 2020, for example, but the DETR image model probably needs to be ran for each image at the same time which might take up quite a bit of GPU together. The `main.py` script looks like a nice and fairly short typical training script you'd be able to quickly run if you download their repo, pull the scienceQA dataset and send the training args to see if it crashes.

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__lawless t1_j76vq7h wrote

Just finished reading. Although imho not a very fair comparison with GPT it still is super impressive

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yaosio t1_j76vwr2 wrote

I think it's likely the ability to determine what is true and what isn't will come from a capability of the model rather than it being told what is and isn't true. It's not possible to mark text as true or not true as this assumes whomever is mafking these things is the sole authority on the truth and never makes mistakes.

At a certain level of capability the AI will be able to use all of its knowledge to determine what is and isn't true. For example, if you know enough about physics and the Earth, you'll know that the sky is blue without seeing it. For something that can't be confirmed or denied, such as, "Bob puts his shoes on before his pants." The AI could determine the likelihood of such an action based on what it knows about Bob, pants, and shoes.

If it's trained on lies it could determine they are lies because the data is not consistent. If I train you that every number plus another number is a number, but 2+2 is special and equals chair, you could determine I'm lying because it's not consistent with all the data as a whole.

Truth has a consistency to it that lies don't have, and a model can learn that.

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__lawless t1_j76xpgk wrote

Just 2 points a) They fine tuned this model to death. Where as GPT3.5 has a handful of examples to fine tune b) This is a multi modal model which consumes the image directly. Where as GPT can only consume text, so they fed it caption of the image

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Lopsided-Factor-780 t1_j7743wv wrote

Question from a noob:
When they say H_Fuse is fed into the decoder model, such that Y = Decoder(H_Fuse), how is it fed in? Is it fed in like the encoder output in an encoder-decoder transformer with cross-attention? Or something else?

Also, if there is a separate encoder and decoder component, are they trained together or separately?

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ThirdMover t1_j77bf6z wrote

> I think it's likely the ability to determine what is true and what isn't will come from a capability of the model rather than it being told what is and isn't true. It's not possible to mark text as true or not true as this assumes whomever is mafking these things is the sole authority on the truth and never makes mistakes.

I think there is a bit of a misunderstanding here. The issue isn't that GPT3 has wrong opinions about stuff. The issue is that it doesn't have any opinions about what is real or isn't whatsoever. Of course any future AI will operate on limited and flawed information and thus have opinions that are not perfectly true. But before we can even get to that point a model needs to even have the idea of "real" and "not real" as fundamental categories. For GPT3 everything is just text, Harry Potter is as real as Obama. Maybe I am wrong and inference can actually get you there through pure consistency checks, as you say. But we will have to see about that.

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ipoppo t1_j77l1hr wrote

Taking from Judea Pearl's book, capability of coming up with useful counterfactuals and causalities will likely built upon foundation of having good assumption about "world model(s)"

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astonzhang t1_j79i4jj wrote

Hi, I am an author of the paper. Opinions below are my own.

​

After we arXiv-ed our "Automatic Chain of Though Prompting in Large Language Models" paper in Oct 2022 (here's a TLDR, ICLR'23), we were asking ourselves:

"If AGI (artificial general intelligence) is the goal, what kind of chain of thought (CoT) research do we need next? Is relying on a text-only generalist model that can perform text-only multitasks the final answer?"

"How can we connect the dots between NLP and CV communities so more researchers can contribute?"

"Since not everyone can afford playing with large models, how can we deal with input in more general form (text and images) *without* relying on larger models so a larger research community can contribute?"

​

One day I was teaching my kid how to solve arithmetic reasoning problems (not from the MultiArith dataset...). My kid told me that it's much easier to understand reasoning problems with the help from figure illustrations.

"Oh, can we leverage vision input to improve chain of thought reasoning?"

"The current generalist models like GPT-3.5 (text-davinci-002/003) only offer a blackbox API (at a cost) for transforming text input into text output. Why not just fine-tune a smaller model where we have full control of all its layers (whitebox) to fuse inputs in a more general form?"

​

Fortunately, Pan Lu et al. released the ScienceQA benchmark, just in time. This is a great contribution to the community and we benefited from it by testing our idea early on this benchmark (see acknowledgement in our GitHub repo). Showing the promise of fine-tuning a smaller model with task-specific datasets (rather than feeding in-context learning demos to a larger generalist LLM) is exactly what we wanted in this study (you may feel more motivated after reading the T-Few paper).

If you feel motivated to try parameter-efficient fine-tuning (PEFT) ideas from the aforementioned T-Few paper to improve Multimodal-CoT, you may also wish to check out our recent PEFT design space paper at ICLR'23 (here's a TLDR).

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Dr_Love2-14 t1_j7aqm6x wrote

During model training, I imagine the model would benefit from some form of "self-reflection" at recurrent intervals, similar to human sleep. For a crude workflow, one could design the model to recall through auto-prompting onto a context window everything its learned that is relevant to the newly exposed training data, and then the model makes a rationale decision (following a constant pre-encoded prompt) to restate the information and classify it as factual or non-factual, and then this self-generated text is backpropagated to the model.

(Disclaimer: I follow ML research as a layman)

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42gauge t1_j7e9mb2 wrote

> If I train you that every number plus another number is a number, but 2+2 is special and equals chair, you could determine I'm lying because it's not consistent with all the data as a whole.

If I train you that every animal isn't conscious, but humans are special and conscious, you could "determine" I'm lying because it's not consistent with all the data as a whole.

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lwl t1_j8hoxpg wrote

Super interesting work, thank you for sharing! If you are still active on reddit - we noticed that the pdf is no longer available on arxiv, are you able to say why that is?

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IluvBsissa t1_j9j9ml9 wrote

Dr. Zhang, thank you so much. Please can you tell us more about your model's performance ? How would it do on standard MMLU ? Can it be improved by increasing parameters count ? The paper didn't mention if the human testers were average human or experts ?

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JClub t1_jabyh73 wrote

GPT was never trained with image data, why is this a fair comparison? The UnifiedQA model is from 2022, so it doesn't seem fair either. Why don't we have some comparisons with other SOTA multimodal models? Such as OFA or UniT

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JClub t1_jabyhe8 wrote

GPT was never trained with image data, why is this a fair comparison? The UnifiedQA model is from 2020, so it doesn't seem fair either. Why don't we have some comparisons with other SOTA multimodal models? Such as OFA or UniT

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JClub t1_jabyi76 wrote

GPT was never trained with image data, why is this a fair comparison? The UnifiedQA model is from 2022, so it doesn't seem fair either. Why don't we have some comparisons with other SOTA multimodal models? Such as OFA or UniT

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