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
@cohere
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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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LLM Use Cases and Practical Applications Explained
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Read more about these use cases and the examples mentioned: https://
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Text Classification Techniques and Applications
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7/ Classify. Here we want to group a list of text pieces into two or more classes. Example: Classify the sentiment of a social post into positive, negative, or neutral.
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Document Clustering: Organizing and Relating Large Article Collections
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6/ Cluster. Take a group of documents and make sense of how they are organized and related to each other. Example: Segment a huge list of articles into themes and topics.
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Search and Similarity: Finding Relevant Documents with AI
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5/ Search/Similarity. Given a text query, find documents that are most similar to the query. Example: Take a customer inquiry and find the most relevant FAQ from a database.
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AI Generates Book Synopses: Try Our New Creative Tool
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Earlier this week, the world celebrated International Book Lovers Day. So this week's preset is all about books. Enter a book name (make something up!) and our AI model will generate a synopsis. Try it out yourself in our Playground – https://t.co/30GOP00ZU1 pic.twitter.com/syk61GjCx1
— Cohere (@cohere) 11 novembre 2022Earlier this week, the world celebrated International Book Lovers Day. So this week's preset is all about books. Enter a book name (make something up!) and our AI model will generate a synopsis. Try it out yourself in our Playground – https://
hubs.li/Q01s6X8t0