Search Results for author:
Found 11 papers, 9 papers with code
LLaMA: Open and Efficient Foundation Language Models
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Ranked #3 on
Question Answering
on OBQA
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
Ranked #4 on
Zero-Shot Video Question Answer
on NExT-GQA
Unsupervised Translation of Programming Languages
We train our model on source code from open source GitHub projects, and show that it can translate functions between C++, Java, and Python with high accuracy.
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Pre-training text representations have led to significant improvements in many areas of natural language processing.
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks.
Mixtral of Experts
In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.
Ranked #9 on
Question Answering
on PIQA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
Target Conditioning for One-to-Many Generation
In this paper, we propose to explicitly model this one-to-many mapping by conditioning the decoder of a NMT model on a latent variable that represents the domain of target sentences.
HyperTree Proof Search for Neural Theorem Proving
With a similar computational budget, we improve the state of the art on the Lean-based miniF2F-curriculum dataset from 31% to 42% proving accuracy.
Ranked #1 on
Automated Theorem Proving
on Metamath set.mm
(Pass@32 metric)
