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Found 13 papers, 8 papers with code
What Makes Large Language Models Reason in (Multi-Turn) Code Generation?
Prompting techniques such as chain-of-thought have established themselves as a popular vehicle for improving the outputs of large language models (LLMs).
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
Large language models (LLMs) deployed as agents solve user-specified tasks over multiple steps while keeping the required manual engagement to a minimum.
Meta Large Language Model Compiler: Foundation Models of Compiler Optimization
To address this gap, we introduce Meta Large Language Model Compiler (LLM Compiler), a suite of robust, openly available, pre-trained models specifically designed for code optimization tasks.
The Larger the Better? Improved LLM Code-Generation via Budget Reallocation
On the other hand, in scenarios where unit-tests are unavailable, a ranking-based selection of candidates from the smaller model falls short of the performance of a single output from larger ones.
Large Language Models for Compiler Optimization
We explore the novel application of Large Language Models to code optimization.
Code Llama: Open Foundation Models for Code
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #34 on
Code Generation
on MBPP
Leveraging Demonstrations with Latent Space Priors
Starting with a learned joint latent space, we separately train a generative model of demonstration sequences and an accompanying low-level policy.
Hierarchical Skills for Efficient Exploration
We alleviate the need for prior knowledge by proposing a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner.
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War
We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy.
STARDATA: A StarCraft AI Research Dataset
We provide full game state data along with the original replays that can be viewed in StarCraft.
Convolutional Sequence to Sequence Learning
The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks.
A Convolutional Encoder Model for Neural Machine Translation
The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence.
Ranked #7 on
Machine Translation
on IWSLT2015 German-English
