Search Results for author:
Found 20 papers, 0 papers with code
AIR-JPMC@SMM4H’22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English).
AIR-JPMC@SMM4H’22: BERT + Ensembling = Too Cool: Using Multiple BERT Models Together for Various COVID-19 Tweet Identification Tasks
This paper presents my submission for Tasks 1 and 2 for the Social Media Mining of Health (SMM4H) 2022 Shared Tasks competition.
AIR-JPMC@SMM4H’22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling
We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition.
Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
Language models are capable of memorizing detailed patterns and information, leading to a double-edged effect: they achieve impressive modeling performance on downstream tasks with the stored knowledge but also raise significant privacy concerns.
CodeMirage: Hallucinations in Code Generated by Large Language Models
We propose the first benchmark CodeMirage dataset for code hallucinations.
BuDDIE: A Business Document Dataset for Multi-task Information Extraction
Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia.
Search-based Optimisation of LLM Learning Shots for Story Point Estimation
One of the ways Large Language Models (LLMs) are used to perform machine learning tasks is to provide them with a few examples before asking them to produce a prediction.
Translating between SQL Dialects for Cloud Migration
In this work, we consider the difficulties of this migration for SQL databases.
DocLLM: A layout-aware generative language model for multimodal document understanding
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities.
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models.
Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter Data
A large body of literature tries to solve this problem by adapting models trained on the source domain to the target domain.
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation.
Ranked #1 on
Question Answering
on ConvFinQA
AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English).
Towards Earnings Call and Stock Price Movement
Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors.
Empirical Study on Detecting Controversy in Social Media
Companies and financial investors are paying increasing attention to social consciousness in developing their corporate strategies and making investment decisions to support a sustainable economy for the future.
Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data
It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects.
Social and Information Networks
Data Sets: Word Embeddings Learned from Tweets and General Data
In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data.
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter.
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs.
