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Found 24 papers, 15 papers with code
UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution
This paper describes our submission to SemEval-2019 Task 12 on toponym resolution over scientific articles.
Target Word Masking for Location Metonymy Resolution
Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources.
KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning
Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information.
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation
However, research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets across different languages.
CMMLU: Measuring massive multitask language understanding in Chinese
As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.
The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models
By carefully designing the framework and the prompts, we are able to overcome a number of challenges, including bug-specific modeling, the large problem scope, the non-deterministic nature of LLMs, etc.
Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging.
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs.
Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU
In this work, we introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia.
Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE
To bridge this gap, we present ACLUE, an evaluation benchmark designed to assess the capability of language models in comprehending ancient Chinese.
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks.
Location Aware Modular Biencoder for Tourism Question Answering
To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity.
Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents.
A Chinese Dataset for Evaluating the Safeguards in Large Language Models
Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs.
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models.
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image.
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed.
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning
Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.
CULG: Commercial Universal Language Generation
Pre-trained language models (PLMs) have dramatically improved performance for many natural language processing (NLP) tasks in domains such as finance and healthcare.
MultiSpanQA: A Dataset for Multi-Span Question Answering
Raw questions and contexts are extracted from the Natural Questions dataset.
