Papers by Khanh Nguyen
Hallucination Detection for Grounded Instruction Generation (2023.findings-emnlp)
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| Challenge: | Existing models for generating instructions for navigation generate references to objects or actions that are inconsistent with what a human follower would perform or encounter along the path. |
| Approach: | They propose a weakly supervised approach that detects hallucinated references by using a pre-trained vision-language model. |
| Outcome: | The proposed model outperforms baseline models and supervised models on generating navigation instructions. |
Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning (D19-1)
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| Challenge: | HANNA is an interactive photo-realistic simulator that can help agents with navigation tasks . human assistants are rich external knowledge sources but may not be available all the time to provide guidance . |
| Approach: | They develop a photo-realistic mobile agent simulator that asks for help from humans . they use natural language and visual instructions to direct agents towards the goals . |
| Outcome: | The proposed approach can accomplish tasks more effectively than competing models . it can also predict its own chances of making future progress . |
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play (2024.emnlp-demo)
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Sha Li, Revanth Gangi Reddy, Khanh Nguyen, Qingyun Wang, Yi Fung, Chi Han, Jiawei Han, Kartik Natarajan, Clare Voss, Heng Ji
| Challenge: | Complex news events require swift responses from government and society, authors say . relying on historical events to project the future is insufficient, they say - a simulator for complex news events is needed . |
| Approach: | They propose a controllable complex news event simulator guided by event schema and user-provided assumptions . they incorporate a geo-diverse commonsense and cultural norm-aware knowledge enhancement component . |
| Outcome: | The proposed simulator achieves higher coherence and appropriateness than existing models. |
Global Voices: Crossing Borders in Automatic News Summarization (D19-54)
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| Challenge: | a crowd-sourced dataset is needed to evaluate cross-lingual summarization methods . human-written summarizing is expensive and difficult to design for humans . |
| Approach: | They construct a multilingual dataset for evaluating cross-lingual summarization methods . they use social-network descriptions of news articles to extract evaluation data . |
| Outcome: | The proposed dataset compares a translate-then-summarize approach with baselines in 15 languages. |
Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models (2023.findings-acl)
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| Challenge: | Recent work examines the cognitive capabilities of language models through psychological tests designed for humans. |
| Approach: | They propose to use human-like cognitive capabilities to evaluate language models . they propose to augment language models with better listeners to improve their performance . |
| Outcome: | The proposed method boosts language models with better models of the listener and improves them. |
Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting Corrections (2024.emnlp-main)
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| Challenge: | Existing systems that only provide instructions generate inaccurate instructions . however, language models can still guide humans toward making sound decisions . |
| Approach: | They develop a system that can detect and correct errors in natural language instructions . it can also be used to narrow down search space and reduce misguidance . |
| Outcome: | The proposed system achieves a 13% increase in success rate and a 29% reduction in final location error distance with 80 users. |
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation (2023.findings-emnlp)
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| Challenge: | Open-source dataset of code-text pairs for training large language models to understand code is outperforms other datasets for code generation and understanding tasks. |
| Approach: | They propose to extract high-quality code-text pairs from a dataset of 43 million pairs . they use rules and deep learning to ensure that the code-sampled samples contain high-quality pairs a . |
| Outcome: | The Vault dataset outperforms existing models on common coding tasks . authors hope the results will propel AI research and software development forward . |