Felix Hill @FelixHill84
Research Scientist, Deepmind I try to think hard about everything I tweet, esp on 90s football and 80s music None of my opinions are really someone else's fh295.github.io London, England Joined July 2010-
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What can be learned about causality and experimentation from passive data? What could language models learn from simply passively imitating text? We explore these questions in our new paper: “Passive learning of active causal strategies in agents and language models” Thread: 1/
Emergence is nothing new, but it *is* something we need to learn a lot more about!
Emergence is nothing new, but it *is* something we need to learn a lot more about!
Anyone know a paper / citation that verifies that LMs trained on code reason better? Would be cool if true according to experiments not just anecdotes
Anyone know a paper / citation that verifies that LMs trained on code reason better? Would be cool if true according to experiments not just anecdotes
Very nice work grounding LLMs to a simulated environment
Very nice work grounding LLMs to a simulated environment
Does anyone still think neural networks are missing a System 2?
@FelixHill84 Nice thread! One addition is that any paper involving humans (users, annotators, evaluators) should already have an ethics application, and any papers involving newly collected data should have a data statement that is prepared before the study starts.
There has been much noise around the fathers and godfathers of Artificial Intelligence, but how about its mothers? Let's start with Ada Lovelace, often regarded as the first computer programmer, and wrote the first algorithm meant to be run on a computer.
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2K Followers 3K Following Sciency sciencer sciencing the science.Great demonstration of an important point that can't be repeated enough — it's hard to interpret LM evaluations if you don't know whether examples appeared in the training data.
As an example, for popular datasets like CoNLL03, ChatGPT is capable of generating the training, validation, and even test splits. It turns out that ChatGPT has been evaluated as a zero-shot or few-shot NER system on this dataset by multiple papers. 🧵2/5
@ayazdanb I'm not missing that point. I'm actually *making* that point. How do we reproduce the type of "pretraining" human and animal babies go through in the first few weeks and months of life?
What can be learned about causality and experimentation from passive data? What could language models learn from simply passively imitating text? We explore these questions in our new paper: “Passive learning of active causal strategies in agents and language models” Thread: 1/
@FelixHill84 arxiv.org/abs/2305.11790…. @miltos1 , @FelixHill84 not the main focus of what you asked about but there's definitely an aspect of better 'reasoning' when prompted in code like constructs even for NLP tasks
If you're interested in some of my past discussions on language and richer learning environments: x.com/andrewlampinen… Or our work on how richer, more embodied environments improve compositional generalization: arxiv.org/abs/1910.00571
But I do think a grounded, social learner might learn more efficiently, generalize better, etc. (cf arxiv.org/abs/2106.00737)! So I hope to see more positive arguments for learning beyond language, based on experimental demonstrations of the benefits. 12/12
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@FelixHill84 @gneubig @tallinzen @kanishkamisra @egrefen Regarding this, I was wondering, do you have ideas for alternative methodologies? I mean, do we absolutely have to pretrain from scratch two models to compare? (Not only for this question, in general, to answer whether x improves y)
@tallinzen @FelixHill84 @kanishkamisra @LChoshen @egrefen Sorry to be late to the party! We definitely thought about similar things, but it's hard to train models in a controlled setting that are nonetheless good enough to demonstrate some of the interesting properties we'd like to test. I'd love to be on the email thread too :)
@FelixHill84 @kanishkamisra @LChoshen @egrefen @gneubig we're on it, just emailed you about it!
@FelixHill84 @LChoshen @egrefen @gneubig I agree! Though I doubt there exists a pair of publicly available pretrained models that satisfy these criteria, and I guess few people know about the precise differences in the training data of code davinci vs regular davinci
@FelixHill84 @LChoshen @egrefen @gneubig Oh yes it’s linked in the Reddit - my bad! Here’s the link: arxiv.org/abs/2210.07128
@FelixHill84 HELM is a good place to check. Looks like some but not all reasoning benchmarks follow this pattern, e.g. crfm.stanford.edu/helm/latest/?g… vs crfm.stanford.edu/helm/latest/?g…
@FelixHill84 Yes, that does seem to be the case. I'm pushing out a detailed report later this week. Will post the link!
@FelixHill84 See the recent @gneubig and co paper
@FelixHill84 Probably not exactly the kind of reasoning you had in mind but @najoungkim and I have some results that LLMs pre-trained on code are better at tracking the state of entities: arxiv.org/abs/2305.02363 (Section 4)
@FelixHill84 Not a paper but this was the first blogpost to make the connection between CoT and pretraining on code iirc yaofu.notion.site/How-does-GPT-O… Though we have seen models like UL2 be able to do CoT without explicit code pretraining.
@FelixHill84 Definitely not representative of all types of reasoning but my paper with @RayzJulia and @AllysonEttinger shows code pretrained models to be no better than non code pretrained models (Fig 6, appendix C.1), so perhaps something to be vary about! aclanthology.org/2023.eacl-main…