When fact-checking methods based on large language models (LLMs) use external evidence to validate claims, knowledge conflicts often arise. These conflicts typically stem from inconsistencies between the external evidence and LLMs' internal pre-existing knowledge. Such an inconsistency could lead LLMs to draw incorrect answers when validating claims, especially when they are overly confident in their internal incorrect knowledge. Previous works on LLM-based fact-checking have overlooked this issue. This paper, for the first time, proposes a framework (namely KnowFC) to navigate this issue. Our key insight is dividing and adaptively utilizing the knowledge that LLMs know and do not know, thereby avoiding conflicts while enhancing the correctness and efficiency of fact-checking. Specifically, in KnowFC, we propose an adaptive retrieval method, where we train an LLM using a reinforcement learning algorithm coupled with the Dunning-Kruger effect-inspired reward mechanism to identify its knowledge boundaries through confidence calibration, thereby realizing adaptive evidence retrieval. Besides, we propose a reliable and debiased fact verification method, where we organize and construct reasoning graphs using retrieved evidence to verify claims, followed by a causal intervention method using causal mediation analysis to mitigate internal knowledge interference. Experimental results on both FEVEROUS and AVeriTeC datasets show that our method outperforms baseline methods in terms of accuracy and F1 score, while also improving fact-checking efficiency.

Retrieval-augmented generation (RAG) has become a cornerstone for enhancing large language models (LLMs) with real-time information from the Web, but its performance often heavily depends on the quality of the retrieved documents. Given that RAG systems frequently draw from vast and often noisy Web corpora, ensuring the reliability of retrieved content is paramount. While rerankers improve the factual accuracy of the RAG system by elevating the proportion of ground-truth documents (GD) in high-ranked results, the shifts of document type distributions during reranking remain unclear, hindering the understanding of the reranker's behavior. To bridge this gap, we conduct an empirical study to categorize documents and compare their distribution before and after reranking. We reveal a counterintuitive finding: though rerankers improve the proportion of GD, they also significantly increase the proportion of harmful documents (HD) in top-ranked retrieved documents. It not only narrows the potential context window for ranking the GD higher but also increases the risk of HD misleading the LLMs, potentially leading to the generation and propagation of misinformation across Web platforms. Motivated by this finding, we propose a risk-aware reranking method for RAG with LLMs, which balances the risk and benefit during reranking. Given a query, the RAG framework first retrieves relevant documents. Then, our approach quantifies the potential beneficial and harmful impacts of various documents on the LLMs' generation. To estimate the impacts, we conduct a dual-aspect document impact assessment via information gain, which employs a risk clipping to avoid the numerical fluctuations in the estimation. Finally, we conduct the reranking according to the potential impact of each document, enabling the reranker to significantly reduce the HD proportion. Experiments and analysis across multiple models and datasets, including Wikipedia, web news, and research papers, show the effectiveness of our method. Our code is available at https://github.com/lzz335/hidden_risk_of_reranking.

Rumor detection is essential for building a responsible web and internet ecosystem, which has attracted significant attention from the research community. However,emerging topic rumor detection, i.e., identify rumors at the early stages of a topic's emergence where only limited discussions can be observed, still remains a challenge. Technically, this scenario is accompanied by the issues ofdata scarcityon emerging topics and thedata distribution discrepancybetween old topics and emerging new topic. In this paper, we propose a new framework termedLLM-drivenADversarialExampleSynthesis (LADES) for emerging topic rumor detection. LADES utilizes Large Language Models (LLMs) for generating readable and contextually coherent adversarial examples. The generated adversarial examples not only expand the training set to tackle the data scarcity issue, but also act as a bridge to connect the data distribution of old and new topics. To overcome training instability in adversarial example generation, LADES introduces a gradient-free Markov Chain Monte Carlo (MCMC) sampling method. This method ensures adversarial examples are readable and contextually coherent by harnessing LLMs, while promoting effective attacks through entropy-based sampling that targets model uncertainty. To mitigate the impact of potential mislabeling in synthetic data, LADES implements a meta-mixed-learning mechanism. This mechanism dynamically adjusts the weights of synthetic adversarial examples, guided by limited labeled data from emerging topics, thereby alleviating the data noise.

Large language models (LLMs) demonstrate significant potential in various applications; however, they are susceptible to generating hallucinations, which can lead to the spread of online misinformation. Existing studies address hallucination detection by (1) employing reference-based methods that consult external resources for verification or (2) utilizing reference-free methods that mainly estimate answer uncertainty based on LLM's internal states. However, reference-based methods incur significant costs and can be infeasible for obtaining reliable external references. Besides, existing uncertainty estimation (UE) methods often overlook the impact of scenario backgrounds inherited from the query's lexical resources, leading to noise in UE. In almost all real-world applications, users care about the uncertainty concerning semantics or facts instead of the query's scenario information. Therefore, we argue that mitigating scenario-related noise and focusing on semantic information can yield a more desirable UE. In this paper, we introduce a plug-and-play scenario-independent framework to enhance unsupervised UE in LLMs by removing scenario-related noise and focusing on semantic information. This framework is compatible with most existing UE methods, as it leverages only the existing UE methods' outputs. Specifically, we design a scenario-specific sampling to paraphrase queries, maintaining their common semantics while diversifying the scenario distribution. Subsequently, to estimate the contribution of the common semantics, we design a factor analysis (FA) model to disentangle the UE score obtained from the given UE method into a combination of multiple latent factors, which represent the contribution of the common semantics and scenario-related noise. By solving the FA model, we decompose the impact of the most significant factor to approximate the uncertainty caused by the common semantics, thus achieving scenario-independent UE. Extensive experiments and analysis across multiple models and datasets demonstrate the effectiveness of our approach.

The recent progress in graph representation learning boosts the development of many graph classification tasks, such as protein classification and social network classification. One of the mainstream approaches for graph representation learning is the hierarchical pooling method. It learns the graph representation by gradually reducing the scale of the graph, so it can be easily adapted to large-scale graphs. However, existing graph pooling methods discard the original graph structure during downsizing the graph, resulting in a lack of graph topological structure. In this paper, we propose a multi-scale graph neural network (MSGNN) model that not only retains the topological information of the graph but also maintains the key-subgraph for better interpretability. MSGNN gradually discards the unimportant nodes and retains the important subgraph structure during the iteration. The key subgraphs are first chosen by experience and then adaptively evolved to tailor specific graph structures for downstream tasks. The extensive experiments on seven datasets show that MSGNN improves the SOTA performance on graph classification and better retains key subgraphs.

Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In this paper, we find that providing hashtags to social media tweets can help alleviate this issue because hashtags can enrich short and ambiguous tweets in terms of various information, such as topic, sentiment, and stance. This motivates us to propose a novel Hashtag-guided Tweet Classification model (HashTation), which automatically generates meaningful hashtags for the input tweet to provide useful auxiliary signals for tweet classification. To generate high-quality and insightful hashtags, our hashtag generation model retrieves and encodes the post-level and entity-level information across the whole corpus. Experiments show that HashTation achieves significant improvements on seven low-resource tweet classification tasks, in which only a limited amount of training data is provided, showing that automatically enriching tweets with model-generated hashtags could significantly reduce the demand for large-scale human-labeled data. Further analysis demonstrates that HashTation is able to generate high-quality hashtags that are consistent with the tweets and their labels. The code is available at https://github.com/shizhediao/HashTation.

As massive conversation examples are easily accessible on the Internet, we are now able to organize large-scale conversation corpora to build chatbots in a data-driven manner. Multi-modal social chatbots produce conversational utterances according to both textual utterances and vision signals. Due to the difficulty of bridging different modalities, the dialogue generation model of chatbots falls into local minima that only capture the mapping between textual input and textual output, as a result, it almost ignores the non-textual signals. Further, similar to the dialogue model with plain text as input and output, the generated responses from multi-modal dialogue also lack diversity and informativeness. In this paper, to address the above issues, we propose a Multi-View Meta-Learning (MultiVML) algorithm that groups samples in multiple views and customizes generation models to different groups. We employ a multi-view clustering to group the training samples so as to attend more to the unique information in non-textual modality. Tailoring different sets of model parameters for each group boosts the genereation diversity via meta-learning. We evaluate MultiVML on two variants of the OpenViDial benchmark datasets. The experiments show that our model not only better explore the information from multiple modalities, but also excels baselines in both quality and diversity.