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Found 51 papers, 14 papers with code
Co-attention based Multimodal Factorized Bilinear Pooling for Internet Memes Analysis
Memes are a new type of social media communication found on social platforms.
Image2tweet: Datasets in Hindi and English for Generating Tweets from Images
The aim is to generate a specialized text like a tweet, that is not a direct result of visual-linguistic grounding that is usually leveraged in similar tasks, but conveys a message that factors-in not only the visual content of the image, but also additional real world contextual information associated with the event described within the image as closely as possible.
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.
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations
The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI.
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.
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.
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.
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination.
A Survey of Hallucination in Large Foundation Models
Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information.
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.
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.
Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?
A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning.
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.
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.
Memotion Analysis through the Lens of Joint Embedding
Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys.
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages
NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media communications in multi-lingual societies like India, Mexico, Europe, parts of USA etc.
Hater-O-Genius Aggression Classification using Capsule Networks
In this paper, we propose an end-to-end ensemble-based architecture to automatically identify and classify aggressive tweets.
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
Fighting an Infodemic: COVID-19 Fake News Dataset
This is further exacerbated at the time of a pandemic.
Hostility Detection Dataset in Hindi
In this paper, we present a novel hostility detection dataset in Hindi language.
Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020).
SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling
To better understand the problem of LM for CM, we initially experimented with several statistical language modeling techniques and consequently experimented with contemporary neural language models.
Aggression and Misogyny Detection using BERT: A Multi-Task Approach
In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection. This paper presents our system for TRAC-2 shared task on {``}Aggression Identification{''} (sub-task A) and {``}Misogynistic Aggression Identification{''} (sub-task B).
NIT\_Agartala\_NLP\_Team at SemEval-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in Twitter Social Media Corpora
The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on {`}Identifying and Categorizing Offensive Language in Social Media{'} by the {`}NIT{\_}Agartala{\_}NLP{\_}Team{'}.
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017
This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform.
Measuring the Limit of Semantic Divergence for English Tweets.
In human language, an expression could be conveyed in many ways by different people.
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content
Several experiments were carried out on the corpora to classify the ethical values of users, incorporating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycholinguistic lexica, speech-acts, and non-linguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Random Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics.
Comparing the Level of Code-Switching in Corpora
Social media texts are often fairly informal and conversational, and when produced by bilinguals tend to be written in several different languages simultaneously, in the same way as conversational speech.
