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Keyword: code generation ×Clear all

8 claims from 4 papers

1. Computer Science › Topic Modeling

   PaLM: Scaling Language Modeling with Pathways

   Chowdhery, Narang, Devlin et al. · arXiv (Cornell University) · 2022

   The authors trained PaLM, a 540-billion-parameter language model, and report state-of-the-art few-shot results on hundreds of benchmarks, plus analyses of scaling, bias, toxicity and memorisation.

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   1. UncheckedThe authors report that scaling a language model up to 540 billion parameters gave state-of-the-art few-shot results on hundreds of benchmarks.“We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.”
   2. UncheckedMany BIG-bench tasks showed sudden, steep gains in performance when the model reached the largest size the authors trained, PaLM 540B.“A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model.”
   3. UncheckedOn some tasks, the 540-billion-parameter PaLM model beat the finetuned state of the art on multi-step reasoning and average human performance on BIG-bench.“On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.”

2. Computer Science › Software Engineering Research

   A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages

   Chen, Tworek, Jun et al. · arXiv (Cornell University) · 2021

   The paper introduces Codex, a GPT model fine-tuned on public GitHub code, tests its Python code writing on a new benchmark, and discusses limitations and the wider impacts of code generation tools.

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   1. UncheckedOn the HumanEval set, the authors' Codex model solved 28.8% of programming problems, against 0% for GPT-3 and 11.4% for GPT-J.“On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%.”
   2. UncheckedSampling 100 solutions per problem from the Codex model produced a working solution for 70.2% of the authors' HumanEval problems.“Using this method, we solve 70.2% of our problems with 100 samples per problem.”
   3. UncheckedThe authors report that their Codex code model struggles with docstrings describing long chains of operations and with binding operations to variables.“Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables.”

3. Computer Science › Topic Modeling

   A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets

   Laskar, Bari, Rahman, Bhuiyan, Joty and Huang · arXiv (Cornell University) · 2023

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   1. Unchecked“We also report a new emergent ability to follow multi-query instructions that we mostly found in ChatGPT and other instruction-tuned models.”

4. Computer Science › Evolutionary Algorithms and Applications

   Evolution through Large Models

   Lehman, Jonathan, Jain, Ndousse, Yeh and Stanley · arXiv (Cornell University) · 2022

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   1. Unchecked“These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain.”