Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models. However, there is no clear conclusion as to which part of the model these hallucinations originate from. In this paper, we present an in-depth investigation into the object hallucination problem specifically within the CLIP model, which serves as the backbone for many state-of-the-art vision-language systems. We unveil that even in isolation, the CLIP model is prone to object hallucinations, suggesting that the hallucination problem is not solely due to the interaction between vision and language modalities. To address this, we propose a counterfactual data augmentation method by creating negative samples with a variety of hallucination issues. We demonstrate that our method can effectively mitigate object hallucinations for CLIP model, and we show the the enhanced model can be employed as a visual encoder, effectively alleviating the object hallucination issue in LVLMs.

Although large language models (LLMs) acquire extensive world knowledge and some reasoning abilities, their proficiency in generating humorous sentences remains a challenge. Previous research has demonstrated that the humor generation capabilities of ChatGPT are confined to producing merely 25 unique jokes. In this work, we concentrate on endowing LLMs with the ability of generating puns, a particular category of humor by preference learning method. We propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences. Specifically, we improve the Direct Preference Optimization (DPO) algorithm to address the challenge of multi-objective alignment problem. Besides, to facilitate further advancement in this field, we collect a Chinese Pun (ChinesePun) dataset, containing 2.1k puns and corresponding annotations. Experimental results on both Chinese and English benchmark datasets demonstrate that our method significantly outperforms all the baseline models.

“This paper provides a comprehensive review of the CCL23-Eval Task 8, i.e., Chinese EssayFluency Evaluation (CEFE). The primary aim of this task is to systematically identify the typesof grammatical fine-grained errors that affect the readability and coherence of essays writtenby Chinese primary and secondary school students, and then to suggest suitable corrections toenhance the fluidity of their written expression. This task consists of three distinct tracks: (1)Coarse-grained and fine-grained error identification; (2) Character-level error identification andcorrection; (3) Error sentence rewriting. In the end, we received 44 completed registration forms,leading to a total of 130 submissions from 11 dedicated participating teams. We present theresults of all participants and our analysis of these results. Both the dataset and evaluation toolused in this task are available1.”