natural language processing – Page 6

What I Read: Research Directions

By Andrew Fairless on February 21, 2024January 7, 2024

https://nlpnewsletter.substack.com/p/nlp-research-in-the-era-of-llms NLP Research in the Era of LLMs5 Key Research Directions Without Much ComputeSebastian RuderDec 19, 2023 “In an era where running state-of-the-art models requires a garrison of expensive GPUs,Continue readingWhat I Read: Research Directions

What I Read: Instruction Tuning

By Andrew Fairless on February 19, 2024December 19, 2023

https://gaotianyu.xyz/blog/2023/11/30/instruction-tuning/ Teach Llamas to Talk: Recent Progress in Instruction TuningTianyu Gao30 November 2023 “…open-ended instruction tuning… fine-tunes an LLM such that it can follow user instructions…. there have been numerousContinue readingWhat I Read: Instruction Tuning

What I Read: Limits of Transformers on Compositionality

By Andrew Fairless on February 13, 2024December 19, 2023

https://arxiv.org/abs/2305.18654 Faith and Fate: Limits of Transformers on CompositionalityNouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, JenaContinue readingWhat I Read: Limits of Transformers on Compositionality

What I Read: survey LLM tooling

By Andrew Fairless on February 8, 2024February 5, 2024

https://ericmjl.github.io/blog/2024/2/1/an-incomplete-and-opinionated-survey-of-llm-tooling/ An (incomplete and opinionated) survey of LLM toolingEric J. Ma1/31/24 7:00 PM “More critical than specifying specific tools, my goal with this blog post is to outline a frameworkContinue readingWhat I Read: survey LLM tooling

What I Read: Multi-Modal Retrieval-Augmented Generation

By Andrew Fairless on February 7, 2024December 19, 2023

https://blog.llamaindex.ai/evaluating-multi-modal-retrieval-augmented-generation-db3ca824d428?gi=45acebfc0a3a Evaluating Multi-Modal Retrieval-Augmented GenerationLlamaIndexNov 16 “A natural starting point is to consider how evaluation was done in traditional, text-only RAG and then ask ourselves how this ought to beContinue readingWhat I Read: Multi-Modal Retrieval-Augmented Generation

What I Read: Adversarial Attacks on LLMs

By Andrew Fairless on February 6, 2024December 19, 2023

https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/ Adversarial Attacks on LLMsLilian WengOctober 25, 2023 “Adversarial attacks are inputs that trigger the model to output something undesired.”

What I Read: Finetuning LLMs Using LoRA

By Andrew Fairless on February 5, 2024December 18, 2023

https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)Sebastian Raschka, PhDNov 19, 2023 “Low-rank adaptation (LoRA) is among the most widely used and effective techniques for efficiently training customContinue readingWhat I Read: Finetuning LLMs Using LoRA

What I Read: Multimodality

By Andrew Fairless on January 4, 2024November 7, 2023

https://huyenchip.com/2023/10/10/multimodal.html Multimodality and Large Multimodal Models (LMMs)Chip HuyenOct 10, 2023 “For a long time, each ML model operated in one data mode… However, natural intelligence is not limited to justContinue readingWhat I Read: Multimodality

What I Read: Finetuning LLMs with LoRA and QLoRA

By Andrew Fairless on January 3, 2024November 7, 2023

Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of ExperimentsSebastian RaschkaOctober 12, 2023 “LoRA is one of theContinue readingWhat I Read: Finetuning LLMs with LoRA and QLoRA

What I Read: Distributed Training, Finetuning

By Andrew Fairless on December 20, 2023November 7, 2023

https://sumanthrh.com/post/distributed-and-efficient-finetuning/ Everything about Distributed Training and Efficient FinetuningSumanth R HegdeLast updated on Oct 13, 2023 “practical guidelines and gotchas with multi-GPU and multi-node training”

Tag: natural language processing