Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. Each quintuple comprises five elements: subject, object, aspect, opinion and preference. With the rise of Large Language Models (LLMs), existing work primarily focuses on enhancing the performance of COQE task through data augmentation, supervised fine-tuning and instruction tuning. Instead of the above pre-modeling and in-modeling design techniques, we focus on innovation in the post-processing. We introduce a model-unaware adaptive chain-of-feedback (COF) method from the perspective of inference feedback and extraction revision. This method comprises three core modules: dynamic example selection, self-critique and self-revision. By integrating LLMs, COF enables dynamic iterative self-optimization, making it applicable across different baselines. To validate the effectiveness of our approach, we utilize the outputs of two distinct baselines as inputs for COF: frozen parameters few-shot learning and the SOTA supervised fine-tuned model. We evaluate our approach on three benchmarks: Camera, Car and Ele. Experimental results show that, compared to the few-shot learning method, our approach achieves F1 score improvements of 3.51%, 2.65% and 5.28% for exact matching on the respective dataset. Even more impressively, our method further boosts performance, surpassing the current SOTA results, with additional gains of 0.76%, 6.54%, and 2.36% across the three datasets.

The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated methods to aid humans in this task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an automated framework to evaluate the performance of large multimodal models. We further propose a retrieval-aware tuning approach for efficient and effective multimodal document reading. Compared to existing works, our benchmark consists of more recent and lengthy documents with hundreds of pages, while also requiring open-ended explanations and not just extractive answers. To our knowledge, our training framework is the first to directly address the retrieval setting for multimodal long documents. To enhance open models, we construct a training corpus in a fully automatic manner. Experiments show that our tuning approach significantly improves the correctness of model responses by 4.6%.