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Found 10 papers, 6 papers with code
A Critical Evaluation of AI Feedback for Aligning Large Language Models
RLAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (RL), using feedback from a critic model.
The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation
State-of-the-art language generation models can degenerate when applied to open-ended generation problems such as text completion, story generation, or dialog modeling.
Lexi: Self-Supervised Learning of the UI Language
Along with text, these resources include visual content such as UI screenshots and images of application icons referenced in the text.
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, model-guiding and reward-based learning, in order to make recommendations on which type of feedback and algorithms work best.
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks.
DIRECTOR: Generator-Classifiers For Supervised Language Modeling
Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness and contradictions.
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Current language generation models suffer from issues such as repetition, incoherence, and hallucinations.
Learning Lexical Subspaces in a Distributional Vector Space
In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics.
A Compositional Approach to Language Modeling
Traditional language models treat language as a finite state automaton on a probability space over words.
Contrastive Entropy: A new evaluation metric for unnormalized language models
In this paper, we address the last problem and propose a new discriminative entropy based intrinsic metric that works for both traditional word level models and unnormalized language models like sentence level models.
