Search Results for author:
Found 26 papers, 17 papers with code
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task.
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach
Resolving dependencies among dialogue history is one of the main obstacles in the research on conversational question answering (QA).
High-Dimensional Interlingual Representations of Large Language Models
We utilize ILO to investigate the impact of single-language fine-tuning on the interlingual representations in multilingual LLMs.
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date.
LLM Internal States Reveal Hallucination Risk Faced With a Query
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness.
Belief Revision: The Adaptability of Large Language Models Reasoning
We evaluate $\sim$30 LMs across diverse prompting strategies and found that LMs generally struggle to appropriately revise their beliefs in response to new information.
High-Dimension Human Value Representation in Large Language Models
The widespread application of LLMs across various tasks and fields has necessitated the alignment of these models with human values and preferences.
Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes.
Contrastive Learning for Inference in Dialogue
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker.
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
We present InstructTODS, a novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue systems that can adapt to diverse domains without fine-tuning.
PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems
Grounding dialogue response generation on external knowledge is proposed to produce informative and engaging responses.
NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages
We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets.
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
It is, for example, better at deductive than inductive reasoning.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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RHO ($ρ$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses.
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling
Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA.
Every picture tells a story: Image-grounded controllable stylistic story generation
Generating a short story out of an image is arduous.
NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages
At the center of the underlying issues that halt Indonesian natural language processing (NLP) research advancement, we find data scarcity.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Towards Answering Open-ended Ethical Quandary Questions
We explore the current capability of LLMs in providing an answer with a deliberative exchange of different perspectives to an ethical quandary, in the approach of Socratic philosophy, instead of providing a closed answer like an oracle.
Can Question Rewriting Help Conversational Question Answering?
Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form.
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Greenformer: Factorization Toolkit for Efficient Deep Neural Networks
While the recent advances in deep neural networks (DNN) bring remarkable success, the computational cost also increases considerably.
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems.
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in the natural language processing (NLP) is slow-moving due to a lack of available resources.
