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topic |
resources |
details |
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| Apr 19 |
introduction |
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Organization, lectures, student topics. |
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| Apr 26 |
foundations |
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Brief recap of generative AI foundations. |
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assignment of topics |
presentation schedule |
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| May 3 |
basics: instruction tuning |
instructGPT |
Language models are pretrained on text corpora, but we want to use them for dialog. The text distributions of text corpora and dialog are quite different. Instruction tuning can be defined as the process of modifying an existing pretrained language model to make it more dialogic. |
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basics: build gpt |
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| May 10 |
instruction tuning (2) |
Koksal et al. |
Reverse instructions: How to leverage existing high-quality user-generated output for creating instructing tuning datasets efficiently and synthetically (EACL/MILA talk). |
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| May 17 |
low-resource multilinguality |
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LLMs have impressive performance for English and several high-resource languages on many tasks. But they perform poorly for most of the world's thousands of languages spoken today. This is mostly due to the fact that large training datasets are a crucial ingredient for successful LLM training today. These large training datasets do not exist for most languages. What are the challenges and opportunities in creating language models for low-resource languages? |
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| May 24 |
MW: continual learning |
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Continual learning aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
This is generally seen as a capability necessary for advanced AI because many tasks encountered by an AI system are new tasks. They can only be solved by leveraging known tasks -- and then not forgetting this new task, but leveraging it in turn for future tasks.
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| May 31 |
AM: memory |
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LLMs use their parameters as memory, but for many tasks an explicit memory is superior to "parametric memory". E.g., LLMs generally do not memorize infrequent facts well, which then leads to hallucinations. Explicity memory also is (in contrast to parametric memory) interpretable, editable, interoperable and scalable -- all of these are properties that are missing from current LLMs and are necessary for many applications for which we would like to use LLMs. We present a new LLM architecture that includes an explicit memory. |
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| June 7 |
CM: multilinguality |
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Scripts pose difficulty for multilingual language models in learning crosslingual knowledge through lexical overlap, e.g., LMs may have difficulty learning that
Russian бутерброд and German Butterbrot refer to the same concept. We refer to this problem as the script barrier. To address this problem, we propose Transliteration Contrastive Modeling (TCM) to finetune LMs by contrasting sentences in its training data and their transliterations in a unified script. This ensures uniformity in the representation space for different scripts. |
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| June 14 |
prompting |
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Generative AI models follow instructions given in natural language. These instructions are referred to as prompts when they are used to "prompt" the language model to solve a task. Good performance on tasks greatly depends on the form of the prompt, which has given rise to prompt engineering. Models can be used on prompts zero-shot or after finetuning. This lecture will cover prompt-based finetuning and prompt engineering. |
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| June 21 |
PL: multilinguality |
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While there has been some progress on masked language models for low-resource
languages, training autoregressive LLMs for low-resource languages is more challenging. For good generative capabilities (natural language generation), one generally needs more training data than for natural language understanding. We present MaLA-500, a generative LLM for low-resource languages, and show that it outperforms previous low-resource LLMs on several evaluation datasets.
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| June 28 |
SZ: robotics |
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In robotics, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions. However, for embodied tasks, where robots interact with complex environments, text-only LLMs often face challenges due to a lack of compatibility with robotic visual perception.
There are many challenges when one wants to
integrate (multimodal) LLMs into various robotic tasks. Recent work that has addressed
these challenges includes determining at which position an object needs to be grasped (e.g., for a potted plant, the pot needs to be grasped, not the plant itself), what natural action is appropriate for addressing a user need (e.g., cleaning up a spill can be done with a sponge) and for navigation (e.g., when looking for a sponge, the kitchen and the bathroom are places where it is most likely to be found).
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| July 5 |
students |
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| July 12 |
students |
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| July 19 |
students |
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