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Found 39 papers, 12 papers with code
Multilingual State Space Models for Structured Question Answering in Indic Languages
The diversity and complexity of Indic languages present unique challenges for natural language processing (NLP) tasks, particularly in the domain of question answering (QA). To address these challenges, this paper explores the application of State Space Models (SSMs), to build efficient and contextually aware QA systems tailored for Indic languages.
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding
Known by more than 1. 5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures.
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization
The rapid rise of large language models (LLMs) has unlocked many applications but also underscores the challenge of aligning them with diverse values and preferences.
On the Feasibility of Vision-Language Models for Time-Series Classification
We develop a novel approach that incorporates graphical data representations as images in conjunction with numerical data.
LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs
The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences.
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval
Notably, on the Italian Cuisine dataset, SKETCH achieved an answer relevancy of 0. 94 and a context precision of 0. 99, representing the highest performance across all evaluated metrics.
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Making analogies is fundamental to cognition.
Visual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
The proliferation of AI techniques for image generation, coupled with their increasing accessibility, has raised significant concerns about the potential misuse of these images to spread misinformation.
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
We introduce ViBe: a large-scale Text-to-Video Benchmark of hallucinated videos from T2V models.
Overview of Factify5WQA: Fact Verification through 5W Question-Answering
Researchers have found that fake news spreads much times faster than real news.
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types
Visual Question-Answering (VQA) has become key to user experience, particularly after improved generalization capabilities of Vision-Language Models (VLMs).
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
However, in this paper, we argue that current image watermarking methods are fragile and susceptible to being circumvented through visual paraphrase attacks.
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
It assesses the complexity of tasks with the Hierarchical Prompting Index (HPI), which demonstrates the cognitive competencies of LLMs across diverse datasets and offers insights into the cognitive demands that datasets place on different LLMs.
Ranked #1 on
Machine Translation
on IWSLT 2017
Investigating Annotator Bias in Large Language Models for Hate Speech Detection
This paper serves as a crucial resource, guiding researchers and practitioners in harnessing the potential of LLMs for data annotation, thereby fostering advancements in this critical field.
Are Small Language Models Ready to Compete with Large Language Models for Practical Applications?
The rapid rise of Language Models (LMs) has expanded their use in several applications.
Decoding the Diversity: A Review of the Indic AI Research Landscape
This work aims to serve as a valuable resource for researchers and practitioners working in the field of NLP, particularly those focused on Indic languages, and contributes to the development of more accurate and efficient LLM applications for these languages.
How Culturally Aware are Vision-Language Models?
Our research compares the performance of four popular vision-language models (GPT-4V, Gemini Pro Vision, LLaVA, and OpenFlamingo) in identifying culturally specific information in such images and creating accurate and culturally sensitive image captions.
A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models
The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks.
A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models
Despite the crucial importance of accelerating text generation in large language models (LLMs) for efficiently producing content, the sequential nature of this process often leads to high inference latency, posing challenges for real-time applications.
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks.
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
OffensiveLang: A Community Based Implicit Offensive Language Dataset
Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords.
PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models
These differences have been attributed to many factors, such as variations in prompting and the specific LLMs used.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
