The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations either neglect to capture the models’ internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn. This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination. Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors. This provides a more faithful semantic-level representation of dependency. Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM’s reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold. Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k. Implementation available here: https://anonymous.4open.science/r/SIRG-1022.

Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events.

Continual few-shot relation extraction (CFRE) aims to continually learn new relations with limited samples. However, current methods neglect the instability of embeddings in the process of different task training, which leads to serious catastrophic forgetting. In this paper, we propose the concept of the following degree from the perspective of instability to analyze catastrophic forgetting and design a novel method based on adaptive gradient correction and knowledge decomposition to alleviate catastrophic forgetting. Specifically, the adaptive gradient correction algorithm is designed to limit the instability of embeddings, which adaptively constrains the current gradient to be orthogonal to the embedding space learned from previous tasks. To reduce the instability between samples and prototypes, the knowledge decomposition module decomposes knowledge into general and task-related knowledge from the perspective of model architecture, which is asynchronously optimized during training. Experimental results on two standard benchmarks show that our method outperforms the state-of-the-art CFRE model and effectively improves the following degree of embeddings.