Codex: Evaluating Large Language Models Trained on Code
A slow description of the paper "Evaluating Large Language Models Trained on Code" by M. Chen et al. posted on arxiv in July 2021.
Timestamps:
00:00 - Evaluating Large Language Models Trained on Code (Codex)
00:21 - Outline
01:02 - Background
02:15 - A language model for code
05:10 - Task and approach
08:36 - Evaluation framework
13:07 - Nuances of pass@k estimation
20:32 - Implementing the pass@k estimator
23:25 - Evaluation details
27:01 - Code Fine-tuning
30:39 - Prompting for evaluation
33:51 - Loss scaling and temperature
36:15 - Model scaling at optimal temperatures
37:12 - Sampling heuristics and BLEU score
40:00 - Comparative Analysis of Related Models
42:32 - Results on the APPS dataset
47:27 - Code generation examples
53:38 - Supervised Fine-tuning
01:01:48 - Supervised Fine-tuning: Results
01:03:41 - Comparing Codex and Codex-S
01:05:08 - Docstring generation
01:10:21 - Limitation: sample efficiency
01:11:21 - Limitation: generation flaws
01:15:45 - Limitation: degradation with docstring length
01:17:10 - Docstring complexity
01:20:30 - Broader Impacts and Hazard Analysis
01:23:58 - Misalignment
01:26:54 - Analysis of Alignment Problems
01:31:03 - Misalignment Results
01:36:51 - Experiment Details
01:40:35 - Bias Analysis
01:43:42 - Bias probes
01:47:38 - Economic Impact
01:51:38 - Economic Impact Analysis
01:56:10 - Economic Impact Analysis: Future directions
01:57:47 - Security implications
02:04:07 - Insecure code generation
02:05:35 - Environmental Impact and Legal Implications
02:08:47 - Risk Mitigation
02:09:53 - Related Work
02:14:32 - Summary
Detailed description:
We start with the background for Codex, which draws inspiration from the successes of scalable sequence prediction in other domains and seeks to reproduce these gains for program synthesis. We describe the task of generating Python functions from docstrings considered by Codex, and how it compares to other models such as GPT-J-6B. We then discuss evaluation and the nuances of the pass@k metric, together with the HumanEval dataset of hand-written coding problems.
Next, we describe how code fine-tuning is performed from GPT-3, and how prompting is used for evaluation. We discuss how the loss scales with model capacity and the influence of temperature. We illustrate the issues with BLEU score as a metric for evaluating code correctness and compare Codex to other code generation models on HumanEval and APPS.
We then turn to code generation examples, where we see a creative solution to prime number checking. We see how supervised fine-tuning on standalone functions (producing Codex-S) yields further gains over Codex, and how the same training data can be inverted to train Codex-D, which maps from functions to docstrings.
We talk about the limitations of the model, which include sample efficiency and difficulty with generating code for complex docstrings. Broader impacts are also discussed, looking at hazard analysis, over-reliance, misalignment, bias, economic impact, security implications, carbon emissions and legal implications under a "fair use" interpretation. We discuss risk mitigations such as content controls and rate limiting. Finally, we conclude with a discussion of previous work on code generation.
Particular thanks are due to Almut Sohpia Koepke for her help with decoding some of the code produced by Codex and other technical details.
Topics: #codex #ai #machinelearning #coding
Slides (pdf): https://samuelalbanie.com/files/diges...
The paper can be found on arxiv here: https://arxiv.org/abs/2107.03374
References for papers mentioned in the video can be found at http://samuelalbanie.com/digests/2022...
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