Blog - AG2

MathChat - An Conversational Framework to Solve Math Problems

TL;DR:

We introduce MathChat, a conversational framework leveraging Large Language Models (LLMs), specifically GPT-4, to solve advanced mathematical problems.
MathChat improves LLM's performance on challenging math problem-solving, outperforming basic prompting and other strategies by about 6%. The improvement was especially notable in the Algebra category, with a 15% increase in accuracy.
Despite the advancement, GPT-4 still struggles to solve very challenging math problems, even with effective prompting strategies. Further improvements are needed, such as the development of more specific assistant models or the integration of new tools and prompts.

Achieve More, Pay Less - Use GPT-4 Smartly

TL;DR:

A case study using the HumanEval benchmark shows that an adaptive way of using multiple GPT models can achieve both much higher accuracy (from 68% to 90%) and lower inference cost (by 18%) than using GPT-4 for coding.

GPT-4 is a big upgrade of foundation model capability, e.g., in code and math, accompanied by a much higher (more than 10x) price per token to use over GPT-3.5-Turbo. On a code completion benchmark, HumanEval, developed by OpenAI, GPT-4 can successfully solve 68% tasks while GPT-3.5-Turbo does 46%. It is possible to increase the success rate of GPT-4 further by generating multiple responses or making multiple calls. However, that will further increase the cost, which is already nearly 20 times of using GPT-3.5-Turbo and with more restricted API call rate limit. Can we achieve more with less?

In this blog post, we will explore a creative, adaptive way of using GPT models which leads to a big leap forward.

Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH

TL;DR:

Just by tuning the inference parameters like model, number of responses, temperature etc. without changing any model weights or prompt, the baseline accuracy of untuned gpt-4 can be improved by 20% in high school math competition problems.
For easy problems, the tuned gpt-3.5-turbo model vastly outperformed untuned gpt-4 in accuracy (e.g., 90% vs. 70%) and cost efficiency. For hard problems, the tuned gpt-4 is much more accurate (e.g., 35% vs. 20%) and less expensive than untuned gpt-4.
AutoGen can help with model selection, parameter tuning, and cost-saving in LLM applications.