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Found 11 papers, 7 papers with code
Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs.
Backdoor Attacks on Dense Passage Retrievers for Disseminating Misinformation
To achieve this, we propose a perilous backdoor attack triggered by grammar errors in dense passage retrieval.
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Motivated by this insight, we developed a Poisoned Sample Identification Module (PSIM) leveraging PEFT, which identifies poisoned samples through confidence, providing robust defense against weight-poisoning backdoor attacks.
LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM).
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term.
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples
To verify this hypothesis, we manually construct a set of counterfactual samples, which modify the original logical forms to generate counterfactual logical forms with rarely co-occurred table headers and logical operators.
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
To deal with this issue, in this paper, we propose a new strategy to perform textual backdoor attacks which do not require an external trigger, and the poisoned samples are correctly labeled.
Dialogue Inspectional Summarization with Factual Inconsistency Awareness
Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary.
Dependency Parsing as MRC-based Span-Span Prediction
The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans.
SemGloVe: Semantic Co-occurrences for GloVe from BERT
In this paper, we propose SemGloVe, which distills semantic co-occurrences from BERT into static GloVe word embeddings.
Investigating Self-Attention Network for Chinese Word Segmentation
Neural network has become the dominant method for Chinese word segmentation.
