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Found 14 papers, 6 papers with code
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization
In this work, we identify a salient correlation between LFQA faithfulness and retrieval heads, a set of attention heads responsible for retrieving contextual information.
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective
To mitigate this issue, we propose MVCL-MI (Maximization of Vision-Language Cross-Lingual Mutual Information), where a visual-text cross-lingual alignment is built by maximizing mutual information between the model's outputs and visual information.
Length Controlled Generation for Black-box LLMs
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications.
Discrete Modeling via Boundary Conditional Diffusion Processes
Experimental results indicate that our approach achieves strong performance in both language modeling and discrete image generation tasks.
Advancing Large Language Model Attribution through Self-Improving
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems.
Extending Context Window of Large Language Models from a Distributional Perspective
However, existing scaling methods often rely on empirical approaches and lack a profound understanding of the internal distribution within RoPE, resulting in suboptimal performance in extending the context window length.
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations.
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
To mitigate this, we further propose a training-free method called Residual Visual Decoding, where we revise the output distribution of LVLMs with the one derived from the residual visual input, providing models with direct access to the visual information.
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis
Large language model unlearning has garnered increasing attention due to its potential to address security and privacy concerns, leading to extensive research in the field.
A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasks
In recent years, Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
This divergence highlights the urgency for a nuanced understanding and comprehensive overview of recent advances in LLM hallucinations.
STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training
Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored.
Controllable Text Generation via Probability Density Estimation in the Latent Space
Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-related classifiers or sampling a representation from relevant discrete samples.
