FLOWGEN: Fast and slow graph generation

Machine learning systems typically apply the same model to both easy and tough cases. This is in stark contrast with humans, who tend to evoke either fast (instinctive) or slow (analytical) thinking depending on the problem difficulty, a property called the dual-process theory of mind. We present FLOWGEN, a graph-generation model inspired by the dual-process theory of mind that generates large graphs incrementally. Depending on the difficulty of completing the graph at the current step, graph generation is routed to either a fast (weaker) or a slow (stronger) model. These modules have identical architectures, but vary in the number of parameters and consequently differ in generative power. Experiments on real-world graphs show that ours can successfully generate graphs similar to those generated by a single large model, while being up to 2x faster.Comment: Accepted at Dynamic Neural Networks Workshop (DyNN), ICML 202

EntiTables: Smart Assistance for Entity-Focused Tables

Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specific tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.Comment: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 201

AutoMix: Automatically Mixing Language Models

Large language models (LLMs) are now available in various sizes and configurations from cloud API providers. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present AutoMix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to AutoMix is a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring training. Given that verifications can be noisy, we employ a meta verifier in AutoMix to refine the accuracy of these assessments. Our experiments using LLAMA2-13/70B, on five context-grounded reasoning datasets demonstrate that AutoMix surpasses established baselines, improving the incremental benefit per cost by up to 89%. Our code and data are available at https://github.com/automix-llm/automix.Comment: The first two authors contributed equally. Work started and partly done during Aman's internship at Google. This version adds results on mixing 3 models, and will be presented at the workshop on robustness of zero/few-shot learning in foundation models, Neurips 202

Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation

Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that generate toxic, inaccurate, and unhelpful content, and automatic evaluation metrics often fail to identify these behaviors. As models become more capable, human feedback is an invaluable signal for evaluating and improving models. This survey aims to provide an overview of the recent research that has leveraged human feedback to improve natural language generation. First, we introduce an encompassing formalization of feedback, and identify and organize existing research into a taxonomy following this formalization. Next, we discuss how feedback can be described by its format and objective, and cover the two approaches proposed to use feedback (either for training or decoding): directly using the feedback or training feedback models. We also discuss existing datasets for human-feedback data collection, and concerns surrounding feedback collection. Finally, we provide an overview of the nascent field of AI feedback, which exploits large language models to make judgments based on a set of principles and minimize the need for human intervention.Comment: Work in Progres

Self-Refine: Iterative Refinement with Self-Feedback

Like people, LLMs do not always generate the best text for a given generation problem on their first try (e.g., summaries, answers, explanations). Just as people then refine their text, we introduce SELF-REFINE, a framework for similarly improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an output using an LLM, then allow the same model to provide multi-aspect feedback for its own output; finally, the same model refines its previously generated output given its own feedback. Unlike earlier work, our iterative refinement framework does not require supervised training data or reinforcement learning, and works with a single LLM. We experiment with 7 diverse tasks, ranging from review rewriting to math reasoning, demonstrating that our approach outperforms direct generation. In all tasks, outputs generated with SELF-REFINE are preferred by humans and by automated metrics over those generated directly with GPT-3.5 and GPT-4, improving on average by absolute 20% across tasks.Comment: Code, data, and demo at https://selfrefine.info