Recent works showed the possibility of building open-vocabulary large language models (LLMs) that directly operate on pixel representations and are implemented as encoder-decoder models that reconstruct masked image patches of rendered text. However, these pixel-based LLMs are limited to autoencoding tasks and cannot generate new text as images. As such, they cannot be used for open-answer or generative language tasks. In this work, we overcome this limitation and introduce PIXAR, the first pixel-based autoregressive LLM that does not rely on a pre-defined vocabulary for both input and output text. Consisting of only a decoder, PIXAR can answer free-form generative tasks while keeping the text representation learning performance on par with previous encoder-decoder models. Furthermore, we highlight the challenges to autoregressively generate non-blurred text as images and link this to the usual maximum likelihood objective. We propose a simple adversarial pretraining that significantly improves the readability and performance of PIXAR making it comparable to GPT2 on short text generation tasks. This paves the way to building open-vocabulary LLMs that are usable for free-form generative tasks and questions the necessity of the usual symbolic input representation -- text as tokens -- for these challenging tasks.

Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively expensive and not scalable. In this work, we propose a new task of simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. The evaluator is specifically designed to assess the quality of the simulated feedback, based on which we decide the best feedback simulator from our proposed variants. On a text-to-SQL dataset, we show that our feedback simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. In low-data settings, our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations.