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Found 20 papers, 8 papers with code
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla
\emph{Circuit analysis} is a promising technique for understanding the internal mechanisms of language models.
Accelerating Large Language Model Decoding with Speculative Sampling
We present speculative sampling, an algorithm for accelerating transformer decoding by enabling the generation of multiple tokens from each transformer call.
Solving math word problems with process- and outcome-based feedback
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks.
Ranked #10 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Fine-Tuning Language Models via Epistemic Neural Networks
Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data.
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Large language models produce human-like text that drive a growing number of applications.
Teaching language models to support answers with verified quotes
We measure the performance of GopherCite by conducting human evaluation of answers to questions in a subset of the NaturalQuestions and ELI5 datasets.
Uncertainty Estimation for Language Reward Models
However, to solve a particular problem (such as text summarization) it is typically necessary to fine-tune them on a task-specific dataset.
Red Teaming Language Models with Language Models
In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases ("red teaming") using another LM.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
College Mathematics
on BIG-bench
(using extra training data)
Improving language models by retrieving from trillions of tokens
We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens.
Ranked #1 on
Language Modelling
on WikiText-103
(using extra training data)
Ethical and social risks of harm from Language Models
We discuss the points of origin of different risks and point to potential mitigation approaches.
Alignment of Language Agents
For artificial intelligence to be beneficial to humans the behaviour of AI agents needs to be aligned with what humans want.
Fine-Tuning Language Models from Human Preferences
Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.
Reward learning from human preferences and demonstrations in Atari
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions.
AI safety via debate
To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences.
Deep Network Guided Proof Search
Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search.
DeepMath - Deep Sequence Models for Premise Selection
We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics.
TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
