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Can LLM-based eval replace human evaluation?

Ehud Reiter

I’ve had several chats over the past month about whether LLM-based evaluation can replace human evaluation. Of course the LLM evaluation must be done well, for example LLMs should not be asked to evaluate their own output (ie, do not ask GPT4 to evaluate text produced by GPT4).

LLM 165
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A Deep Dive into Retrieval-Augmented Generation in LLM

Unite.AI

Research has shown that large pre-trained language models (LLMs) are also repositories of factual knowledge. When fine-tuned, they can achieve remarkable results on a variety of NLP tasks. Chatgpt New ‘Bing' Browsing Feature Prompt engineering is effective but insufficient Prompts serve as the gateway to LLM's knowledge.

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NLP has become much more interesting!

Ehud Reiter

There was a time around 5 years ago when I found it difficult to propose papers which I was excited by, but in the second half of 2023 I’ve been spoilt for choice, there are lots of exciting papers out there. Which G Abercrombie et al (2023). D Demszky et al (2023). G Lapalme (2023). Proc of WMT-2023.

NLP 124
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John Snow Labs Announces Program for the 2023 NLP Summit, the World’s Largest Gathering on Applied Natural Language Processing

John Snow Labs

Topics Covered Include Large Language Models, Semantic Search, ChatBots, Responsible AI, and the Real-World Projects that Put Them to Work John Snow Labs , the healthcare AI and NLP company and developer of the Spark NLP library, today announced the agenda for its annual NLP Summit, taking place virtually October 3-5.

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LLM Output — Evaluating, debugging, and interpreting

Towards AI

Last Updated on December 30, 2023 by Editorial Team Author(s): Lan Chu Originally published on Towards AI. LLMs are not useful if they are not sufficiently accurate. Obviously, the most reliable way to evaluate an LLM system is to create an evaluation dataset and compare the model-generated output with the evaluation set.

LLM 111
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Optimize LLM with DSPy : A Step-by-Step Guide to build, optimize, and evaluate AI systems

Unite.AI

BootstrapFinetune : Distills a prompt-based DSPy program into weight updates for smaller LMs, allowing you to fine-tune the underlying LLM(s) for enhanced efficiency. The post Optimize LLM with DSPy : A Step-by-Step Guide to build, optimize, and evaluate AI systems appeared first on Unite.AI.

LLM 130
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Adapting LLM & NLP Models to Domain-Specific Data 10x Faster with Better Data

John Snow Labs

When building high-performing LLM & NLP systems, most of our time is spent debugging models and iterating over individual issues that lead us to fixing our datasets in an ad-hoc, manual way. The post Adapting LLM & NLP Models to Domain-Specific Data 10x Faster with Better Data appeared first on John Snow Labs.

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