Towards Data Science’s Post

7 Steps to Mastering Large Language Model Fine-tuning From theory to practice, learn how to enhance your NLP projects with these 7 simple steps.

On my reading stack: A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks (a tip of the hat to Raphaël MANSUY, for his recent post citing this paper) https://lnkd.in/gYMh9b5B

"Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective" #AffectiveComputing #LargeLanguageModels #InstructionTuning #PromptEngineering https://lnkd.in/duERXbSD

How can large language models (LLMs) help researchers with the demanding task of reviewing papers? Learn more about LLMs as reviewers and metareviewers! #LLMs #NLProc [2406.16253] LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

It is my pleasure to share with you the Springer book Practical Solutions for Diverse Real-World NLP Applications that I recently published. https://lnkd.in/dtgNbRjz

BERT which first came out in 2018 took the NLP world by storm. Even though we have moved towards generative AI models in NLP, BERT is still being for the encoding part in these models. New AI models such as GPT and RAG models still use BERT as encoders. Wrote this article for Research Graph Foundation, which talks about BERT and its various aspects. Link to the article: https://lnkd.in/g5G8VAC9

Spoiler alert:yes (in NLP). So, if we use the tech to help us develop ideas (vs compete against it as in the experiment), consider what is possible. Also, take note—this is a preprint https://lnkd.in/e2YsnJsu

A great reading on the evaluation of NLP tasks, one of the best articles on the evaluation. A large collective of authors from various research teams and companies (Amazon, Stability AI, EleutherAI, CMU and many other) share practical insights of their work on evaluation of their models. Many of their practices are implemented in the open source evaluation toolset, they present in the article https://lnkd.in/gsGK7EVj Abstract: Effective evaluation of language models remains an open challenge in NLP. Researchers and engineers face methodological issues such as the sensitivity of models to evaluation setup, difficulty of proper comparisons across methods, and the lack of reproducibility and transparency. In this paper we draw on three years of experience in evaluating large language models to provide guidance and lessons for researchers. First, we provide an overview of common challenges faced in language model evaluation. Second, we delineate best practices for addressing or lessening the impact of these challenges on research. Third, we present the Language Model Evaluation Harness (lm-eval): an open source library for independent, reproducible, and extensible evaluation of language models that seeks to address these issues. We describe the features of the library as well as case studies in which the library has been used to alleviate these methodological concerns.

The fourth installment in our series on LLMs is now live on Built In. Benjamin Weinert and I dive into evaluating LLMs, from the difficulties posed by non-determinism to applying standard NLP metrics. Have a read and leave a comment. https://lnkd.in/gigp-yAd

Advanced language models have revolutionized NLP, significantly improving machine understanding and human language generation..... #algorithmic #Analysis #article #Comprehensive #Empirical #language #models #Presents #PreTraining #progress