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Found 7 papers, 5 papers with code
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work.
V-STaR: Training Verifiers for Self-Taught Reasoners
Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on self-generated solutions to improve their problem-solving ability.
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer.
On the Compositional Generalization Gap of In-Context Learning
In this work, we look at the gap between the in-distribution (ID) and out-of-distribution (OOD) performance of such models in semantic parsing tasks with in-context learning.
Understanding by Understanding Not: Modeling Negation in Language Models
To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus.
Ordered Memory
Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory.
Learning to Understand Goal Specifications by Modelling Reward
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.
