Papers by Dianqi Li
Contextualized Perturbation for Textual Adversarial Attack (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing techniques for generating adversarial examples are driven by local heuristic rules that are agnostic to the context, resulting in unnatural and ungrammatical outputs. |
| Approach: | They propose a ContextuaLized AdversaRial Example generation model that generates fluent and grammatical outputs through a mask-then-infill procedure. |
| Outcome: | The proposed model outperforms baseline models in terms of attack success rate, textual similarity, fluency and grammaticality. |
A Mixture of h - 1 Heads is Better than h Heads (2020.acl-main)
Copied to clipboard
| Challenge: | Evidence has shown that multi-head attentive neural architectures are overparameterized. |
| Approach: | They propose a multi-head attentive neural architecture that “reallocates” attention heads to different inputs. |
| Outcome: | The proposed model outperforms baselines on machine translation and language modeling tasks. |
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models (2024.findings-emnlp)
Copied to clipboard
Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, Dinesh Manocha
| Challenge: | Large vision-language models are prone to hallucinations, where contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects. |
| Approach: | They propose to automate the generation of hallucination-related questions using images . they propose to use three image manipulation strategies to induce hallucinosity . |
| Outcome: | The proposed approach reduces human bias in crafting such examples and improves accuracy. |
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction. |
| Approach: | They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model. |
| Outcome: | The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers. |
RuleR: Improving LLM Controllability by Rule-based Data Recycling (2025.naacl-short)
Copied to clipboard
| Challenge: | Existing supervised fine-tuning datasets are composed of general instructions without userspecified constraints. |
| Approach: | They propose a data augmentation method incorporating multiple constraints into the original data samples according to predefined rules to create new training tasks. |
| Outcome: | The proposed method improves LLM controllability while maintaining general instruction-following capabilities. |
Domain Adaptive Text Style Transfer (D19-1)
Copied to clipboard
| Challenge: | Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance. |
| Approach: | They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available . |
| Outcome: | The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner. |
Phrase-level Textual Adversarial Attack with Label Preservation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing adversarial attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality. |
| Approach: | They propose a phrase-level perturbation-based adversarial ATtack that generates adversarials through phrase- level perturbations. |
| Outcome: | The proposed approach improves the performance of natural language processing models by reducing the need for word-level perturbations and preserving the fluency and grammaticality of the samples. |
Contextual Text Style Transfer (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for text style transfer are limited by the lack of parallel data. |
| Approach: | They propose a task to translate a sentence into a desired style with its surrounding context taken into account. |
| Outcome: | The proposed model outperforms state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics. |