Welcome to PaperShelf 🗞️🗞️
Join on my academic exploration journey on all things LLMs and Telecom
Recently I started documenting my notes & learnings on interesting academic papers that I explore as part of research journey.
While it started as a childish hobby, I began to realize its potential to be useful for many others, who simply want to get a TLDR version of these papers. So if you are someone who are also interested & looking to track what’s the latest & greatest happening in the world of machine learning, GenAI, LLMs intersecting with network automation, telecommunications & wireless then you will definitely find my reading useful.
On each of my readings, I try to organize my understanding under 3 main topics
What is this paper about ?
In this section, I document what are the major questions this paper is trying to answer along with overall findings & results.
What are the key contributions of this paper ?
In this section, I capture in depth (again as per my understanding) what are they key novel contributions made by this paper.
Here, I’m not speculating or predicting, I simply elucidate the key contributions as highlighted by the authors within the paper.
What are my key takeaways from this paper?
In this section, I reflect what is that I personally learnt from this paper.
Often times, I realize the learnings I get from this paper are not always the key contributions as noted in previous section, but it would be something hidden either in the references or buried deep within one of the phrases of the paper. It is truly mind blowing when something totally unexpected you find within the paper. I tend to document such things in this section.
Here is the summary of my latest 3 readings.
1. TSpec-LLM
An Open-source Dataset for LLM Understanding of 3GPP Specifications.
This paper effectively presents a LLM trained from vast collection of 3GPP specifications and empirically tries to prove that this dataset can actually augment a general purpose LLM to be more 3GPP domain intelligent.
link : https://www.viswakumar.com/papershelf/tspec-llm
2. A Primer on Generative AI for Telecom
NVIDIA’s contribution towards O-RAN Chatbot
This paper basically provides a introductory material for anyone to start connecting the dots between AI and Telecom vertical. At an outset it starts by quoting the fact that AI related research has been long standing within telecom domain, well before the arrival of LLMs in to mix. Later introduces O-RAN chatbot - a fine tuned LLM bot based on O-RAN specifications.
link : https://www.viswakumar.com/papershelf/nvidia-telecom-primer
3. Chain-of-Thought Reasoning without Prompting
Can you make a LLM reason without prompting? Looks like you can…
this paper asks the question Can LLMs reason effectively without prompting? and then proceeds to answer the same. Specifically, by eliciting the Chain of Thought (CoT) reasoning paths, this paper convinces that, we can make LLM provide good reasoned response without explicitly pre-training, fine-tuning or even prompting the LLM explicitly.
Authors of this paper, claim that, this can be achieved by making the LLM auto-magically tap into CoT reasoning path by a special decoding technique called CoT Decoding.
link : https://www.viswakumar.com/papershelf/cot-without-prompting
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