Teaching Algorithmic Reasoning via In-context Learning. A four-stage approach to teaching algorithmic reasoning to LLMs is identified and studied in this work by @oh_that_hat
, @Azade_Na
, @Hugo_Larochelle
, Aaron Courville, @BNeyshabur
, @HanieSedghi
LLMS
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Teaching Algorithmic Reasoning to LLMs via In-context Learning
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Fast DistilBERT Pipeline for CPU Inference Optimization
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Fast DistilBERT on CPUs. @HaihaoShen
, @ZafrirOfir
, Bo Dong, Hengyu Meng, Xinyu Ye, Zhe Wang, Yi Ding, Hanwen Chang, Guy Boudoukh, @MosheWasserblat propose a new pipeline for creating and running Fast Transformer models on CPUs -
Intermediate Models Value Through Transfer Learning Fine-tuning
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Where to start? Analyzing the potential value of intermediate models. By finetuning a model on a source dataset, one may have a better starting point when finetuning a target dataset. @LChoshen
, Elad Venezian, Shachar Don-Yehia, @NoamSlonim
, @YoavKatz -
LLMs Struggle Learning Long-Tail Knowledge From Training Data
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Large Language Models Struggle to Learn Long-Tail Knowledge. @kandpal_nikhil
, @HaikangDeng
, Adam Roberts, @Eric_Wallace_
, @ColinRaffel examine the relationship between their pre-training datasets and their knowledge memorized. -
Top NLP Research Papers November 2022
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If you work in #NLP, it's important to keep up to date with the latest research. In this post, we look at some of the top papers on NLP that were published in November 2022 https://
txt.cohere.ai/top-nlp-papers
-november-2022/
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Configurable AI Models and Safety Mode Control Options
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i think it’s very reasonable for “default models” to be inoffensive, but within some pretty bounds i think people should be able to configure the models to behave how they’d like, eg like turning off safe mode
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Fine-tuned Models Producing Poor Results: Data Quality Issues
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but the results from the fine-tuned models were trash and the responses I was getting were really bad I am assuming it takes either significantly more refined prompt/completion data than what I gave it or just more data (both could be true)
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Initial Fine-Tuning Approach: Breaking Books into Chunks and Generating Questions
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initial approach: oh this seems not too bad, I read this doc https://
beta.openai.com/docs/guides/fi
ne-tuning
… and was like yeah I can just break up the authors' books into chunks and generate some simple questions for each chunk and then use that to fine-tune the model -
Philosophy Author Chatbot Project Using GPT-3 Fine-Tuning
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philosophy author chatbot thing w gpt background: need to create something for my philosophy of AI class idea: fine-tune gpt3 models on text from authors we have read in class and create a chat application where users can talk to these models
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Philosophy Author Chatbot Project Using Fine-tuned GPT-3 Models
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philosophy author chatbot thing w gpt background: need to create something for my philosophy of AI class idea: fine-tune gpt3 models on text from authors we have read in class and create a chat application where users can talk to these models