Papers by Lingjun Zhao
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. |
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval (D19-61)
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| Challenge: | Low resource languages often lack relevance annotations for cross-lingual information retrieval . when available, the training data has limited coverage for possible queries . |
| Approach: | They propose a weakly supervised neural model for Cross-lingual information retrieval from low-resource languages using weak supervision instead of relevance annotations. |
| Outcome: | The proposed model achieves 19 MAP points improvement compared to CNNs and 12 points improvement from machine translation-based CLIR models. |
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. |
Can Hallucination Correction Improve Video-Language Alignment? (2025.findings-acl)
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| Challenge: | Existing work on hallucination correction for large vision-language models focuses on mitigating hallucisations, but a new approach is needed to improve video-language alignment. |
| Approach: | They propose a self-training framework learning to correct hallucinations in descriptions that do not align with the video content. |
| Outcome: | The proposed framework improves video-language alignment by identifying and correcting inconsistencies in descriptions that do not align with the video content. |
A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations (2025.emnlp-main)
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| Challenge: | a measure of faithful free-text explanations is difficult to generate by language models and assess by humans. |
| Approach: | They propose a measure of Prediction-EXplanation consistency by extending the concept of weight of evidence. |
| Outcome: | The proposed measure improves explanation faithfulness by up to 9.7%, the authors show . they show that applying preference optimization improves the consistency of generated explanations across three model families. |
Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach (2020.lrec-1)
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| Challenge: | Existing methods for event extraction are "one size fits all" and are not adaptable to new event types or domains of interest. |
| Approach: | They propose a few-shot Event Mention Retrieval task to retrieve event mentions from text . they use existing event datasets such as ACE and a Siamese Network approach . |
| Outcome: | The proposed approach lowers the bar of specifying event-centric information needs. |
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. |