Models (LLMs) in collaborating with external data, yet significant challenges exist in real-world scenarios. In areas such as academic literature and finance question answering, data are often found in raw text and tables in HTML or PDF formats, which can be lengthy and highly unstructured.

We thank all the reviewers for their encouraging comments. In both these cases, τ is effectively zero. Liu et al. shows how GTD-class algorithms can be formally derived using a primal-dual saddle point Sutton et al. presents a (single time-scale) variant of linear TD learning, which they call emphatic TD and show that They also provide an asymptotic convergence analysis to the set of local optima. If the paper is accepted, we will work further on improving the clarity of the work.

A recent line of work aims at specially prompting LLMs to review their own generations and generate meaningful natural language feedback, which can subsequently be used to refine them [Madaan et al.,

We thank the reviewers for the positive feedback and their interest in our work! Below we address some questions. Both algorithms are well-tuned for hyperparameters. We didn't include it in the submission because after all the We will make sure to define them earlier in the paper in the revision. We are happy to clarify them.

We thank the reviewers for their thoughtful comments. Reviewers have described our work as "extremely important in that it provides a reality check for Reviewers' comments have been paraphrased for brevity. R3: It looks like the random image regularizer hurts in-domain performance. R3: Do other VQA datasets (e.g., GQA, VCR) have the same problem? R2: Do other datasets for OOD evaluation have similar problems like VQA-CP?

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g.,