4.5k citations · 5k across the 22 of their papers we have counts for
22 papers
Planning with Reasoning using Vision Language World Model
Delong Chen, Theo Moutakanni, Willy Chung +4
Effective planning requires strong world models, but high-level world models that can understand and reason about actions with semantic and temporal abstraction remain largely unde…
WorldPrediction: A Benchmark for High-level World Modeling and Long-horizon Procedural Planning
Delong Chen, Willy Chung, Yejin Bang +2
Humans are known to have an internal "world model" that enables us to carry out action planning based on world states. AI agents need to have such a world model for action planning…
HalluLens: LLM Hallucination Benchmark
Yejin Bang, Ziwei Ji, Alan Schelten +5
Large language models (LLMs) often generate responses that deviate from user input or training data, a phenomenon known as "hallucination." These hallucinations undermine user trus…
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations
Ziwei Ji, Lei Yu, Yeskendir Koishekenov +6
LLMs often adopt an assertive language style also when making false claims. Such ``overconfident hallucinations'' mislead users and erode trust. Achieving the ability to express in…
Delusions of Large Language Models
Hongshen Xu, Zixv yang, Zichen Zhu +7
Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high beli…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
LLM Internal States Reveal Hallucination Risk Faced With a Query
Ziwei Ji, Delong Chen, Etsuko Ishii +4
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recogni…
High-Dimension Human Value Representation in Large Language Models
Samuel Cahyawijaya, Delong Chen, Yejin Bang +5
The widespread application of LLMs across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of hum…
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…
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.…
Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge
Tiezheng Yu, Ziwei Ji, Pascale Fung
Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a given meeting transcript conditioned upon a query. The main challenges for QFMS are the long input text l…
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…
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…
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…
VScript: Controllable Script Generation with Visual Presentation
Ziwei Ji, Yan Xu, I-Tsun Cheng +6
In order to offer a customized script tool and inspire professional scriptwriters, we present VScript. It is a controllable pipeline that generates complete scripts, including dial…
Survey of Hallucination in Natural Language Generation
Ziwei Ji, Nayeon Lee, Rita Frieske +10
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based…
Model Generalization on COVID-19 Fake News Detection
Yejin Bang, Etsuko Ishii, Samuel Cahyawijaya +2
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information. Considering the problematic consequences that the…
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Zihan Liu, Yan Xu, Tiezheng Yu +5
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack doma…
Multi-hop Question Generation with Graph Convolutional Network
Dan Su, Yan Xu, Wenliang Dai +3
Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more c…