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Found 26 papers, 9 papers with code
Enhancing Image Generation Fidelity via Progressive Prompts
We find that deeper layers are always responsible for high - level content control, while shallow layers handle low - level content control.
VASparse: Towards Efficient Visual Hallucination Mitigation for Large Vision-Language Model via Visual-Aware Sparsification
In this work, we propose an efficient plug-and-play decoding algorithm via Visual-Aware Sparsification (VASparse) from the perspective of token sparsity for mitigating VH.
Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation
The Amar framework comprises two key sub-components: 1) a self-alignment module that aligns commonalities among entities, relations, and subgraphs to enhance retrieved text, thereby reducing noise interference; 2) a relevance gating module that employs a soft gate to learn the relevance score between question and multi-aspect retrieved data, to determine which information should be used to enhance LLMs' output, or even filtered altogether.
DisPose: Disentangling Pose Guidance for Controllable Human Image Animation
Specifically, we generate a dense motion field from a sparse motion field and the reference image, which provides region-level dense guidance while maintaining the generalization of the sparse pose control.
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD).
DiffATR: Diffusion-based Generative Modeling for Audio-Text Retrieval
To this end, we present a diffusion-based ATR framework (DiffATR), which models ATR as an iterative procedure that progressively generates joint distribution from noise.
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
The goal of this survey is to explore the current landscape of multimodal affective research, identify development trends, and highlight the similarities and differences across various tasks, offering a comprehensive report on the recent progress in multimodal affective computing from an NLP perspective.
Aspect-Based Sentiment Analysis
Emotion Recognition in Conversation
+3
XMeCap: Meme Caption Generation with Sub-Image Adaptability
Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines.
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities.
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference
Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response to increasing input lengths, challenges memory and time efficiency.
D2O: Dynamic Discriminative Operations for Efficient Generative Inference of Large Language Models
Efficient inference in Large Language Models (LLMs) is impeded by the growing memory demands of key-value (KV) caching, especially for longer sequences.
Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning
SLU usually consists of two subtasks, including intent detection and slot filling.
Textual Inversion and Self-supervised Refinement for Radiology Report Generation
Existing mainstream approaches follow the encoder-decoder paradigm for generating radiology reports.
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause
In this paper, we propose a Unified Multimodal Emotion recognition and Emotion-Cause analysis framework (UniMEEC) to explore the causality between emotion and emotion cause.
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, disparity, adversarial vulnerability, and inconsistency.
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback remains a significant impediment.
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory
Due to two annoying issues in video grounding: (1) the co-existence of some visual entities in both ground truth and other moments, \ie semantic overlapping; (2) only a few moments in the video are annotated, \ie sparse annotation dilemma, vanilla contrastive learning is unable to model the correlations between temporally distant moments and learned inconsistent video representations.
TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction
However, these methods are not able to fully leverage the trigger information and even bring noise to relation extraction.
Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation
Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists.
Improve Retrieval-based Dialogue System via Syntax-Informed Attention
Since incorporating syntactic information like dependency structures into neural models can promote a better understanding of the sentences, such a method has been widely used in NLP tasks.
Exploiting Auxiliary Caption for Video Grounding
Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video.
A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios.
Ranked #1 on
Intent Detection
on MixATIS
