As Picasso once said, “Computers are useless; all they can do is *answer* questions.” LLMs are impressive but they will not replace human creativity, which decides which questions to ask (and which answers to accept).
LLMS
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API Cost Caps Increase Impact on AI Economics
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What “bumping” up cost caps past the threshold feels like IRL.*
— David Herrmann (@herrmanndigital) 2 décembre 2022
* you know if you know pic.twitter.com/EMVQ3fn3lXWhat “bumping” up cost caps past the threshold feels like IRL.* * you know if you know
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Language Dynamics Distillation Improves Policy Learning
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Improving Policy Learning via Language Dynamics Distillation. @hllo_wrld
, @Jayelmnop
, @LukeZettlemoyer
, @EGrefen
, @_rockt propose Language Dynamics Distillation (LDD), which pretrains a model to predict environment dynamics given demonstrations with language descriptions -
Compression Enhances Generalization in Multilingual NER Models
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Intriguing Properties of Compression on Multilingual Models. This paper finds that compression confers several interesting & prev. unknown generalization properties on mBERT NER models. Kelechi Ogueji, Orevaoghene Ahia, @lekeOnilude
, Sebastian Gehrmann, @SaraHookr
, @KreutzerJulia -
HELM: Holistic Evaluation Method for Language Models
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Language Models are Changing AI: The Need for Holistic Evaluation. New benchmarking method, Holistic Evaluation of Language Models (HELM) has been developed at the Center for Research on Foundation Models to help provide transparency in LMs. @RishiBommasani
, @PercyLiang
, Tony Lee -
Teaching Algorithmic Reasoning to LLMs via In-context Learning
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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 -
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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