409 citations · 842 across the 33 of their papers we have counts for
34 papers
Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
Samuel Cahyawijaya, Holy Lovenia, Fajri Koto +13
Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesi…
LLMs Are Few-Shot In-Context Low-Resource Language Learners
Samuel Cahyawijaya, Holy Lovenia, Pascale Fung
In-context learning (ICL) empowers large language models (LLMs) to perform diverse tasks in underrepresented languages using only short in-context information, offering a crucial a…
Subobject-level Image Tokenization
Delong Chen, Samuel Cahyawijaya, Jianfeng Liu +2
Patch-based image tokenization ignores the morphology of the visual world, limiting effective and efficient learning of image understanding. Inspired by subword tokenization, we in…
Contrastive Learning for Inference in Dialogue
Etsuko Ishii, Yan Xu, Bryan Wilie +4
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speake…
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
Willy Chung, Samuel Cahyawijaya, Bryan Wilie +2
Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP), yet remain under-explored for task-oriented dialogue systems (TODS), especially…
Towards Mitigating Hallucination in Large Language Models via Self-Reflection
Ziwei Ji, Tiezheng Yu, Yan Xu +3
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. However, the practical deployment still faces…
Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
Holy Lovenia, Wenliang Dai, Samuel Cahyawijaya +2
Object hallucination poses a significant challenge in vision-language (VL) models, often leading to the generation of nonsensical or unfaithful responses with non-existent objects.…
PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems
Bryan Wilie, Yan Xu, Willy Chung +3
Grounding dialogue response generation on external knowledge is proposed to produce informative and engaging responses. However, current knowledge-grounded dialogue (KGD) systems o…
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
Samuel Cahyawijaya, Holy Lovenia, Willy Chung +3
Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hi…
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Wenliang Dai, Junnan Li, Dongxu Li +6
Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-lan…
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
Yan Xu, Deqian Kong, Dehong Xu +4
The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system. Common strategies…
InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning
Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu +2
Large language models (LLMs) that are tuned with instructions have demonstrated remarkable capabilities in various tasks and languages. However, their ability to generalize to unde…
Learn What NOT to Learn: Towards Generative Safety in Chatbots
Leila Khalatbari, Yejin Bang, Dan Su +4
Conversational models that are generative and open-domain are particularly susceptible to generating unsafe content since they are trained on web-based social data. Prior approache…
Which One Are You Referring To? Multimodal Object Identification in Situated Dialogue
Holy Lovenia, Samuel Cahyawijaya, Pascale Fung
The demand for multimodal dialogue systems has been rising in various domains, emphasizing the importance of interpreting multimodal inputs from conversational and situational cont…
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee +10
This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of…
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji +44
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Thro…
RHO (): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ziwei Ji, Zihan Liu, Nayeon Lee +4
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce halluci…
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling
Samuel Cahyawijaya, Bryan Wilie, Holy Lovenia +4
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. However, the appli…
Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values
Yejin Bang, Tiezheng Yu, Andrea Madotto +3
Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. Yet, human values can vary under diverse cultural conditions. There…
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
Wenliang Dai, Zihan Liu, Ziwei Ji +2
Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information. In this paper, we system…
Context Generation Improves Open Domain Question Answering
Dan Su, Mostofa Patwary, Shrimai Prabhumoye +6
Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either direct…
Every picture tells a story: Image-grounded controllable stylistic story generation
Holy Lovenia, Bryan Wilie, Romain Barraud +3
Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriatel…
Kaggle Competition: Cantonese Audio-Visual Speech Recognition for In-car Commands
Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu +2
With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-ca…