Search Results for author:
Found 21 papers, 13 papers with code
A Zero-Shot Claim Detection Framework Using Question Answering
In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection.
MimeQA: Towards Socially-Intelligent Nonverbal Foundation Models
Socially intelligent AI that can understand and interact seamlessly with humans in daily lives is increasingly important as AI becomes more closely integrated with peoples' daily activities.
The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination
To address it, we propose a novel concept: knowledge overshadowing, where model's dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details.
Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models
This phenomenon partially stems from training data imbalance, which we verify on both pretrained models and fine-tuned models, over a wide range of LM model families and sizes. From a theoretical point of view, knowledge overshadowing can be interpreted as over-generalization of the dominant conditions (patterns).
MACAROON: Training Vision-Language Models To Be Your Engaged Partners
Large vision-language models (LVLMs), while proficient in following instructions and responding to diverse questions, invariably generate detailed responses even when questions are ambiguous or unanswerable, leading to hallucinations and bias issues.
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation
In this paper, we first investigate the potential of LVLM on multimodal misinformation detection.
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences.
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning
Image Captioning
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R-Tuning: Instructing Large Language Models to Say `I Don't Know'
This approach is formalized by first identifying the disparity in knowledge encompassed by pre-trained parameters compared to that of instruction tuning data.
Defining a New NLP Playground
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks.
Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs
Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) community.
SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts
Timely and comprehensive understanding of emerging events is crucial for effective decision-making; automating situation report generation can significantly reduce the time, effort, and cost for intelligence analysts.
NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
Norm discovery is important for understanding and reasoning about the acceptable behaviors and potential violations in human communication and interactions.
A Weibo Dataset for the 2022 Russo-Ukrainian Crisis
Online social networks such as Twitter and Weibo play an important role in how people stay informed and exchange reactions.
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
