Watson Chua’s Post

A great article on the end-to-end, nuts and bolts, process of building an AI solution. From the dirty data, to the cost of deployment. Any moment now, an "LLMOps" charlatan will pop out of the weeds...

Check out the recent fine-tuning work by the Govtech AI Practice and see the impact of technology in the public sector. Watson Chua Yuzhang FENG (Ph.D.) Leslie Teo

And (hopefully) very soon the full text of the LegalTech special issue will be published! But in the meantime read the online first articles by András Osztovits, Péter Homoki, Ricardo Lillo, Dóra Pálfi dr. https://lnkd.in/dJMiq8X4

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We are also working on applying LLMs as a judge for the relevance of information retrieval/search systems for data. So, it is great to see this type of application of LLMs gaining more attention! Gathering relevance dataset to measure improvements or adverse impacts of changes to search/retrieval systems is challenging. LLMs can scale this for us, provided sufficient human baselines are established! #AI #AIforData #llm4data

Great quick starter for using LLM as a judge! Many works have explored using LLM as a judge for point-wise, pair-wise and ranking evaluations. Based on my recent reading, here are some limitations identified and methods proposed to address them: ✅ Position bias -> swapping positions ✅ Knowledge bias -> reference guided judge ✅ Limited reasoning capabilities -> COT + reference guided judge + Actor Critic ✅ Fine-tuning for evaluation further addresses the limitation and improved reliability. Examples include JudgeLM and Arena fine-tuned Vicuna. 🤔 Although promising results have been shown for LLM evaluations, challenges remain for multimodal LLM-as-a-judge, especially gaps from human preferences in point-wise scoring and ranking evaluations. The main issues identified include biases, hallucination and inconsistencies. References: https://lnkd.in/g6JBbzK3 https://lnkd.in/gTgPRARX https://lnkd.in/gWAkSfjV

My comment on this article: It seems to me that this approach resembles how we humans memorize facts. For example, if we are taking a history class, we might first read a chapter to get an overview. Then, knowing that the history test will requires us recalling exact dates, we might then go back over the chapter looking for dates which we would write on a piece of paper with what happen on that date. The night before the test, we would review the dates on our paper. We don’t run 100 epochs over the data, but enough times to get a good enough grade on the test. But if the history professor told us that it would be an open book test, then we would bring in the book so that we could look up the exact dates. Then instead of memorizing dates, we would focus on understanding the concepts but also how to quickly find the dates in the book. This seems more like the RAG (Retrieval-Augmented Generation) approach being used by many LLMs where a specific database is accessed or by doing queries into the internet. I suspect the ability to do both approaches will be found in future LLMs.

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