LLMs for Code Seminar: Uri Alon (CMU) — Jan 23
LLMs for Code Seminar
Hi everyone,
We are very excited to have Uri Alon (CMU) this Wednesday, Jan 23, at 11am EST.
Title: Natural Language Reasoning with Language Models of Code.
Abstract: In this talk, I will show that LMs that were pretrained on *code* can be better natural language reasoners than LMs that were trained (mostly) on natural language, even when the task does not involve source code at all.
In a class of structured NL reasoning tasks, I will show how we can frame the task as code generation; this makes LMs of code such as Codex better reasoners than LMs of natural language such as T5 and GPT-3.
Another class of mathematical reasoning tasks was recently unlocked by methods that require LLMs to generate their explicit reasoning steps, such as “chain-of-thought” (Wei et al., 2022).
Such methods employ LMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LMs seem adept at the step-by-step decomposition part, they often make logical and arithmetic mistakes in the solution part. I will show how LMs of *code* can decompose the natural language problem into runnable steps, which allows us to offload the solution to a programmatic runtime such as a Python interpreter. That is, instead of learning to solve the problem directly, we teach the model to generate a program that solves the problem. Across a variety of benchmarks, this approach leads to more accurate results than much larger models such as PALM-540B using chain-of-thought.
Please register for this session.
You will receive a confirmation email with a Zoom link.
Best,
Nadav Timor
