Data Machina #189
LLM research topics. Common Sense in chatbots. MarioGPT. TikTok's recommender embeddings. Piloting a GPT bot in a law firm. Stanford Prompting, Finetuning & RLHF. Focal modulation vs. Self-Attention.
Some Research Topics on Language Models. There’s so much stuff happening around LLMs that is challenging to filter what to read. Here’s my two cents on some interesting LLM research in the last 15 days.
Augmenting LLMs. Originally, LLMs were not designed for search, calculation, knowledge retrieval, or symbolic tasks. Inevitably, building LLM apps requires augmentation. This paper gives you an overview on the latest in: Augmented Language Models: a Survey.
On a practical note, LangChain and GPTIndex are excellent tools for augmenting and extending LLMs. Checkout these 2 links below:
How to build a Q&A bot on documentation with ChatGPT & LangChain
A team @Standford_NLP released the source code of DSP: Demonstrate–Search–Predict Framework, which enables you to build rich interactions between retrieval models (RMs) and language models (LMs.) This is great for developing complex Q&A or conversational search bots.
LLM model development. Developing LLMs is very inefficient. One common issue is that developers have to frequently process the same pieces of text over and over again. If you read How to Build a Chatbot with GPT-3 you can tell how many times the developer has to copy and run the original prompt.
Aspects like prompt templates and prompt chaining can help solving inefficiencies. This week, to address this issue, a team of researchers @Allen_AI introduced a new way to make LLMs development more sustainable with Embedding Recycling.
Triggered by the fact that LLMs involves so many different tasks and layers of computation, a team @MSResearch launched a new initiative called LLMOps for building LLM products. That’s right Large Language Model Operations ;-)
Developing a conversational agent that is able to reason while demonstrating common sense is a bit of an ultimate goal in AI chatbot development. Here’s a good read Common Sense Reasoning for Conversational AI: A Survey of the State of the Art.
Generalist vs. specialised LLMs. This is a classic in ML: generalisation vs specialisation. There are many methods to build and improve generalist LLMs. These researchers have developed AdapterSoup, which uses weight averaging to improve generalisation of pretrained LMs.
Still is not yet truly known how a generalist model can perform many NLP tasks in a zero-shot approach. In this paper: Is ChatGPT a General-Purpose NLP Task Solver? researchers discuss the challenges of LLMs generalisation in depth.
There is a lot of demand for developing specialised LLMs for enterprise verticals. A way to build bespoke LLMs for specialised domains is through sophisticated prompting. In enjoyed reading these 2 papers on specialisation & prompting:
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
LLMs for AI pair-programming & coding. GPT/Codex-based tools are great for code prototyping, and low to mid level pair-programming. But If you want to scale and industrialise code development, How do you evaluate the code generated by the LLM? Enter CodeBERTScore: a new way to evaluate code generation with pretrained models of code
Some developer are fascinated by how LLMs can support programming tasks. David wrote a long post on why ChatGPT Is An Extra-Ordinary Python Programmer.
Efficient training, inference & fine-tuning of LLMs. Fine-tuning LLMs and inference are very computationally expensive. Many researchers are finding new, super-efficient ways to reduce computational costs. Here is a new, SoTA approach that addresses LLM computational challenges: PEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models on Low-Resource Hardware.
On a similar line of research, the Big Little Transformer Decoder is a new framework developed to improve inference efficiency and latency for a wide range of LLM applications.
A team of researchers @CMU & @HPE, developed a new, general cross-modal fine-tuning framework, that achieves SoTA across several LLM tasks
Gaming & LLMs. Since ages, gaming has been one of the main drivers of AI research. This week I came across two really interesting papers on gaming and LLMs:
Here’s a playable demo of MarioGTP. Enjoy!
you can Have a nice week.
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Stanford CS224N: Prompting, Finetuning & RLHF (2023 slides, pdf)
What Happened When a Top 5 UK Law Firm Piloted Harvey GPT Bot
I Required My Students to Use ChatGPT. This is What I Learned
the ML Pythonista
[Tutorial] Focal Modulation: A Replacement for Self-Attention
the ML codeR
Deep & Other Learning Bits
AI/ DL ResearchDocs
Google Research: Scaling Vision Transformers to 22 Billion Parameters
pyCirclize: Create Beautiful Circular Visualisations in Python
[Free course] ML Engineering for Production (MLOps) Specialization
AI startups -> radar
ML Datasets & Stuff
Tips? Suggestions? Feedback? email Carlos
Curated by @ds_ldn in the middle of the night.