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Found 57 papers, 13 papers with code
Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement
Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity.
FACTOID: FACtual enTailment fOr hallucInation Detection
We present FACTOID (FACTual enTAILment for hallucInation Detection), a benchmark dataset for FE.
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
We have fine-tuned an LLM with injected [PAUSE] tokens, allowing the LLM to pause while reading lengthier prompts.
Refining Text-to-Image Generation: Towards Accurate Training-Free Glyph-Enhanced Image Generation
We introduce a benchmark, LenCom-Eval, specifically designed for testing models' capability in generating images with Lengthy and Complex visual text.
Optical Character Recognition (OCR)
Text-to-Image Generation
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs
In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter.
PHAnToM: Personality Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Drawing inspiration from psychological research on the links between certain personality traits and Theory-of-Mind (ToM) reasoning, and from prompt engineering research on the hyper-sensitivity of prompts in affecting LLMs capabilities, this study investigates how inducing personalities in LLMs using prompts affects their ToM reasoning capabilities.
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text.
A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization
We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text.
Cause and Effect: Can Large Language Models Truly Understand Causality?
The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions
The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution.
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings
Embeddings play a pivotal role in the efficacy of Large Language Models.
Born With a Silver Spoon? Investigating Socioeconomic Bias in Large Language Models
Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds.
AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach
Probing LLMs with varied iterations of a single question could reveal potential inconsistencies in their knowledge or functionality.
Developing a Framework for Auditing Large Language Models Using Human-in-the-Loop
An effective method is to probe the LLM using different versions of the same question.
Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders.
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation
Our proposed DM-SFDA method involves fine-tuning a pre-trained text-to-image diffusion model to generate source domain images using features from the target images to guide the diffusion process.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters.
Post-Training Embedding Alignment for Decoupling Enrollment and Runtime Speaker Recognition Models
Automated speaker identification (SID) is a crucial step for the personalization of a wide range of speech-enabled services.
Gaussian Adaptive Attention is All You Need: Robust Contextual Representations Across Multiple Modalities
We propose the Multi-Head Gaussian Adaptive Attention Mechanism (GAAM), a novel probabilistic attention framework, and the Gaussian Adaptive Transformer (GAT), designed to enhance information aggregation across multiple modalities, including Speech, Text and Vision.
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey
The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP), contributing to substantial progress in both text comprehension and generation.
MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries
This work introduces the task of multimodal medical question summarization for codemixed input in a low-resource setting.
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded.
CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare
This multimodal approach not only enhances the decision-making process in healthcare but also fosters a more nuanced understanding of patient queries, laying the groundwork for future research in personalized and responsive medical care
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream data.
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
This research explores the problem of deception through the lens of psychology, employing a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence.
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring
Automatic Speech Recognition (ASR) has witnessed a profound research interest.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences.
Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems
Recent advancements in Large Language Models empower them to follow freeform instructions, including imitating generic or specific demographic personas in conversations.
The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations
Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI).
Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index
Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by emergence of techniques to bypass detection.
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation
Domain Adaptation (DA) is a method for enhancing a model's performance on a target domain with inadequate annotated data by applying the information the model has acquired from a related source domain with sufficient labeled data.
Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges
Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of NLP tasks, demonstrating the ability to reason and apply commonsense.
Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes
In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23.
CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling
Therefore, to enrich word information and incorporate positional information, positional encoding is defined.
Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering
Robustness in Natural Language Processing continues to be a pertinent issue, where state of the art models under-perform under naturally shifted distributions.
RESTORE: Graph Embedding Assessment Through Reconstruction
We analyze their effectiveness in preserving the (a) topological structure of node-level graph reconstruction with an increasing number of hops and (b) semantic information on various word semantic and analogy tests.
Artificial Intelligence in Career Counseling: A Test Case with ResumAI
It is difficult to find good resources or schedule an appointment with a career counselor to help with editing a resume for a specific role.
Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi
The recent advances in deep-learning have led to the development of highly sophisticated systems with an unquenchable appetite for data.
Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts.
Findings of Factify 2: Multimodal Fake News Detection
With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent.
Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers
Both PAAT and PAAB surpass their respective backbone Transformers by up to 9. 8% in real-world action recognition and 21. 8% in multi-view robotic video alignment.
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics.
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering
To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering.
IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
We also evaluate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Image2Tweet, and (iii) text-based image retrieval.
ANALOGICAL -- A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models
Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec.
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field.
Factify 2: A Multimodal Fake News and Satire News Dataset
In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles.
Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes
Memes are the new-age conveyance mechanism for humor on social media sites.
Few-shot Multimodal Multitask Multilingual Learning
While few-shot learning as a transfer learning paradigm has gained significant traction for scenarios with limited data, it has primarily been explored in the context of building unimodal and unilingual models.
Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers
The ability to recognize and interpret facial emotions is a critical component of human communication, as it allows individuals to understand and respond to emotions conveyed through facial expressions and vocal tones.
I see what you hear: a vision-inspired method to localize words
This paper explores the possibility of using visual object detection techniques for word localization in speech data.
iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability
We demonstrate the effectiveness of iReason using a two-pronged comparative analysis with language representation learning models (BERT, GPT-2) as well as current state-of-the-art multimodal causality models.
iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering
Most prior art in visual understanding relies solely on analyzing the "what" (e. g., event recognition) and "where" (e. g., event localization), which in some cases, fails to describe correct contextual relationships between events or leads to incorrect underlying visual attention.
Ranked #4 on
Video Question Answering
on TVQA
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks
However, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details, usually seen with CNNs when super-resolving at large upscaling factors.
Ranked #1 on
Video Super-Resolution
on Vimeo90K
iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks
However, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details, usually seen with CNNs when super-resolving at large upscaling factors.
Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval
The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents.
A robust, low-cost approach to Face Detection and Face Recognition
The proposed Face Detection and Recognition system using Discrete Wavelet Transform (DWT) accepts face frames as input from a database containing images from low cost devices such as VGA cameras, webcams or even CCTV's, where image quality is inferior.
