Papers by Robin Wang
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph. |
| Approach: | They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding . |
| Outcome: | The proposed method outperforms RotatE, Distmult and ComplEx on various data sets. |
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems (2022.findings-emnlp)
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| Challenge: | Widely used multi-modal pretrained models generalize poorly to out-of-distribution data, suggesting shortcomings in the VLE2E pipeline. |
| Approach: | They develop a segment-combine test for multi-image queries and contrast set for cross-benchmark transfer. |
| Outcome: | The proposed method shows that it is possible to train both neural and neuro-symbolic models in the same way. |
Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering (2023.emnlp-main)
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| Challenge: | Existing methods for multistep question answering have shown promise in generating multistep solutions, but they lack robustness. |
| Approach: | They propose a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions. |
| Outcome: | The proposed framework outperforms neuro-symbolic methods on a DROP contrast set and GPT-3.5 on QA adversarial sets. |
Proving membership in LLM pretraining data via data watermarks (2024.findings-acl)
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| Challenge: | Detecting whether copyright holders’ works were used in large language model (LLM) training is poised to be an important problem. |
| Approach: | They propose to use data watermarks to enable principled detection with only black-box model access, provided the rightholder contributed multiple training documents and watermarked them before public release. |
| Outcome: | The proposed method can be used to test hypothesis testing on a black-box model . it shows that the watermarks are strong under model and dataset scaling . |
On Continual Model Refinement in Out-of-Distribution Data Streams (2022.acl-long)
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| Challenge: | Existing continual learning (CL) problems cannot cover real-world scenarios such as out-of-distribution errors. |
| Approach: | They propose a continual model refinement problem formulation to solve this problem . they extend several existing continual learning approaches to the CMR problem based on a general sampling algorithm . |
| Outcome: | The proposed model refinement solution improves on existing models and their performance metrics. |
TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability (2025.emnlp-demos)
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Mohammad Aflah Khan, Ameya Godbole, Johnny Wei, Ryan Yixiang Wang, James Flemings, Krishna P. Gummadi, Willie Neiswanger, Robin Jia
| Challenge: | Existing workflows for pretraining large language models are cumbersome, fragmented and inaccessible. |
| Approach: | They propose an open-source library for editing, inspection, and analysis of large language model datasets. |
| Outcome: | TokenSmith is an open-source library for editing, inspection, and analysis of large language model datasets. |
Efficient End-to-End Visual Document Understanding with Rationale Distillation (2024.naacl-long)
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| Challenge: | Pre-processing tools such as optical character recognition (OCR) can map document image inputs to textual tokens, then large language models (LLMs) can reason over text. |
| Approach: | They propose a method that integrates outputs of OCR tools and larger multimodal models as intermediate "rationales" a student model is trained to predict rationales and answers based on visual documents . |
| Outcome: | The proposed model outperforms the base model on three visual document understanding benchmarks with only 1% higher computational cost. |
Cheap Character Noise for OCR-Robust Multilingual Embeddings (2025.findings-acl)
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| Challenge: | Optical character recognition (OCR) is a key component of the digitization of historical documents. |
| Approach: | They propose a method that fine-tunes existing multilingual models using noisy texts and a contrastive loss. |
| Outcome: | The proposed model improves on the training data of existing models using noisy texts and a contrastive loss. |