Sparse Autoencoders Find Highly Interpretable Features in Language Models https://
arxiv.org/abs/2309.08600 @HoagyCunningham @aidanprattewart @loganriggssmith @Robert_AIZI @leedsharkey
LLMS
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Sparse Autoencoders Reveal Interpretable Language Model Features
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Large Language Models as Optimizers Research Paper
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Large Language Models as Optimizers https://
arxiv.org/abs/2309.03409 @chengrun_yang Xuezhi Wang @yifenglou @Hanxiao_6 @quocleix @denny_zhou @xinyun_chen_ -
Draft & Verify: Lossless LLM Acceleration via Self-Speculative Decoding
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Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding https://
arxiv.org/abs/2309.08168
Jun Zhang
Jue Wang
Huan Li
Lidan Shou
Ke Chen
Gang Chen
Sharad Mehrotra -
Headless Language Models: Learning Without Predicting
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Headless Language Models: Learning without Predicting with Contrastive Weight Tying https://
arxiv.org/abs/2309.08351 @nthngdy Éric de la Clergerie @bensagot -
Data Pruning Strategies for Large Language Model Pretraining
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When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale https://
cohere.com/research/paper
s/when-less-is-more-investigating-data-pruning-for-pretraining-llms-at-scale-2023-09-08
… @maxdoesresearch @ahmetustun89 @luizapzbn @W4ngatang @mziizm @sarahookr -
Mixture of Experts Parameter Efficiency for Instruction Tuning
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Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning https://
cohere.com/research/paper
s/pushing-mixture-of-experts-to-the-limit-extremely-parameter-efficient-moe-for-instruction-tuning-2023-09-11
… @tedzadouri Ahmet Üstün @aahmadian_ @beyzaermis @acyr_l @sarahookr -
Top NLP Research Papers September 2023 Curated by Cohere
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Stay up-to-date with the latest research in NLP! Here are some of the top recent papers curated by the Cohere for AI community! https://
txt.cohere.com/top-nlp-papers
-september-2023/
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Featuring the following papers: (thread) -
RAG Reranking: Enhancing Retrieval with Pinecone Cohere
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RAG with reranking – leveraging @pinecone and @cohere Reranking is a retrieval technique that performs an additional step on top of retrieved results This step uses a separate model to rerank the results, making sure the most relevant ones surface to the top @jamescalam
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Advanced Math Solves Language Distance and Name Schema Problems
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Extraordinary that sufficiently advanced math manages to cache obscure org charts while also solving distance-in-pronunciation for all words and languages simultaneously while also, no big deal, solving the problem of the human name schema that has bedeviled every program ever.
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Podcast with Logan Bartlett on AI and Innovation Ideas
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check out my recent podcast with @loganbartlett !
— Alexandr Wang (@alexandr_wang) 3 novembre 2023
he prepared extremely well. we talked about nearly everything AI and some of my best ideas https://t.co/u3uUgND1qZcheck out my recent podcast with @loganbartlett ! he prepared extremely well. we talked about nearly everything AI and some of my best ideas