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Found 19 papers, 2 papers with code
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
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.
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.
