Papers by Raphael Tang
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing (2021.acl-long)
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| Challenge: | Pre-trained language models have improved the state-of-the-art results on many NLP applications. |
| Approach: | They propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget. |
| Outcome: | The proposed regularization improves confidence estimation without increasing computation budget. |
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)
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Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin
| Challenge: | End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference. |
| Approach: | They propose to use a third-party ASR system as a weak supervision source and labeling functions derived from implicit user feedback to reduce human labor. |
| Outcome: | The proposed system improves word-error rate and speed up 600% over third-party ASR. |
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models (2024.naacl-long)
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| Challenge: | Large language models exhibit positional bias in how they use context, which affects listwise ranking. |
| Approach: | They propose a method to marginalize out different list orders in the prompt to produce an order-independent ranking with less positional bias. |
| Outcome: | The proposed method improves on five datasets in sorting and passage reranking by 34-52% . it marginalizes out different list orders in the prompt to produce an order-independent ranking . |
The Role of Mixed-Language Documents for Multilingual Large Language Model Pretraining (2026.acl-long)
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Jiandong Shao, Raphael Tang, Crystina Zhang, Karin Sevegnani, Pontus Stenetorp, Jianfei Yang, Yao Lu
| Challenge: | Existing research suggests that multilingual large language models can achieve impressive cross-lingual understanding despite largely monolingual pretraining. |
| Approach: | They compare a monolingual-only corpus with a standard web corpus that removes all multilingual documents and then retrain the models from scratch under controlled conditions. |
| Outcome: | The results show that removing bilingual data causes translation performance to drop 56% in BLEU, whereas code-switching contributes minimally. |
Natural Language Generation for Effective Knowledge Distillation (D19-61)
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| Challenge: | Knowledge distillation can transfer knowledge from deep language representation models to shallow word embedding-based neural networks. |
| Approach: | They propose to build an unlabeled transfer dataset to enable effective knowledge transfer . they hypothesize that this principled, general approach outperforms rule-based techniques . |
| Outcome: | The proposed method outperforms OpenAI GPT on four datasets in sentiment classification, sentence similarity, and linguistic acceptability. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers (2023.findings-acl)
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| Challenge: | Natural language processing tasks rely on complex neural models . transformer-based models are typically slow to execute, making it a non-trivial challenge to apply them in real-world applications. |
| Approach: | They propose to consider an efficiency method as an operator applied on a model . they find that the commutativity and cumulativeness of efficiency operators are plausible . |
| Outcome: | The proposed method is commutative and cumulative, and the results are estimated by combining methods. |
“Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors (2023.findings-acl)
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| Challenge: | Text classification is one of the most fundamental tasks in natural language processing (NLP), but deep neural networks are data-hungry and expensive to train. |
| Approach: | They propose a non-parametric alternative to DNNs that uses a compressor like gzip and a k-nearest-neighbor classifier to achieve competitive results. |
| Outcome: | The proposed method outperforms BERT on all five OOD datasets and outperformed other methods on the few-shot setting. |
Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting (D19-3)
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| Challenge: | keyword spotting systems are used for simple commands recognition on devices . however, voice-enabled web applications are few and far between . a prominent drawback is that most of these systems perform speech recognition in the cloud . |
| Approach: | Honkling is a JavaScript-based keyword spotting system that can be deployed on user devices. |
| Outcome: | Honkling is a JavaScript-based keyword spotting system that can be deployed on user devices. |
What the DAAM: Interpreting Stable Diffusion Using Cross Attention (2023.acl-long)
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Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
| Challenge: | a new text-image attribution analysis model for text-to-image generation is understudied due to ethical constraints . corporators have restricted the general public from using the models and their weights . |
| Approach: | They perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. |
| Outcome: | The proposed method achieves a competitive 58.8-64.8 mIoU on noun segmentation and fair to good mean opinion scores on all parts of speech rated by humans . it also achieves good attribution quality on all part of speech, rated in humans - and the first to interpret large diffusion models from a visuolinguistic perspective. |
Inserting Information Bottlenecks for Attribution in Transformers (2020.findings-emnlp)
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| Challenge: | Pretrained transformers are a popular approach for understanding features important for prediction. |
| Approach: | They apply information bottlenecks to analyze attribution of features for prediction on a black-box model. |
| Outcome: | The proposed method outperforms two competing methods in degradation tests on four datasets. |
Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation (2024.emnlp-main)
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Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
| Challenge: | Current diffusion models do not cover recent models, thus we curate three test sets for evaluation. |
| Approach: | They propose a human-calibrated measure of variability in a set of images bootstrapped from existing image-pair perceptual distances. |
| Outcome: | The proposed model outperforms nine baselines by 18 points in accuracy and matches graded human judgements 78% of the time. |
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)
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| Challenge: | Semantic similarity modeling is central to many NLP problems such as question answering. |
| Approach: | They propose a pairwise word interaction model with syntactic structure priors to explore their effectiveness. |
| Outcome: | Extensive evaluations on eight benchmark datasets show that incorporating structural information improves over strong baselines. |
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression (2021.eacl-main)
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| Challenge: | Existing methods to make exiting decisions are limited to classification tasks . large-scale pre-trained language models such as BERT have brought performance gain but at the cost of heavy computational burden. |
| Approach: | They propose a fine-tuning strategy and a learning-to-exit module to accelerate BERT inference . they propose to make trade-offs between model quality and efficiency by early exiting . |
| Outcome: | The proposed approach improves early exiting for BERT, with better trade-offs . it can be combined with other acceleration methods, and the proposed strategy can be applied to regression tasks. |
Showing Your Work Doesn’t Always Work (2020.acl-main)
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| Challenge: | countless experimental papers lack empirical rigor, disregarding necessities such as statistical significance tests and computational environments. |
| Approach: | They propose to report the expected validation effectiveness of the best-tuned model with respect to the computational budget. |
| Outcome: | The proposed model favors negative errors and yields poor bootstrapped confidence intervals, the authors argue . they find that the proposed model is biased and uses error-prone assumptions . |
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference (2020.acl-main)
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| Challenge: | Large-scale pre-trained language models such as BERT are notorious for being slow in both training and inference. |
| Approach: | They propose a method to accelerate BERT inference by inserting extra classification layers between each transformer layer of BERT. |
| Outcome: | The proposed method saves up to 40% inference time with minimal degradation in model quality. |
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)
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Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Ifeoluwa Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp
| Challenge: | Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature. |
| Approach: | They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it. |
| Outcome: | The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data. |
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)
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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott
| Challenge: | FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China. |
| Approach: | They evaluate vision–language Models and large language models on unseen food images and corresponding questions. |
| Outcome: | The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions. |
Understanding Retrieval Robustness for Retrieval-augmented Image Captioning (2024.acl-long)
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| Challenge: | Recent retrieval-augmented models for image captioning are not perfect in practice. |
| Approach: | They propose to train a retrieval-augmented captioning model SmallCap by sampling retrieved captions from more diverse sets. |
| Outcome: | The proposed model is sensitive to tokens that appear in the majority of retrieved captions . the proposed model improves both in-domain and cross-domain performance . |
Lost in Embeddings: Information Loss in Vision–Language Models (2025.findings-emnlp)
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| Challenge: | Experiments reveal connectors substantially distort the local geometry of visual representations, with k-nearest neighbors diverging by 40–60% post-projection, correlating with degradation in retrieval performance. |
| Approach: | They propose two approaches to examine and quantify information loss by analyzing latent representation space. |
| Outcome: | The proposed model improves retrieval performance by analyzing changes in k-nearest neighbor relationships between image representations before and after projection. |
Voice Query Auto Completion (2021.emnlp-main)
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Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin
| Challenge: | Existing methods fail to complete voice queries from incomplete prefixes because they use orthographic prefix and substrings instead of the true phonetic prefix. |
| Approach: | They propose to condition QAC approaches on intermediate transcriptions to complete voice queries. |
| Outcome: | The proposed method obtains an 18% relative improvement over previous methods on a speech-enabled smart television with real-life voice search traffic. |
Strings from the Library of Babel: Random Sampling as a Strong Baseline for Prompt Optimisation (2024.naacl-long)
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| Challenge: | Recent prompt optimisation approaches use the generative nature of language models to produce prompts, even rivaling the performance of human-curated prompts. |
| Approach: | They propose to randomly sample tokens from the model vocabulary as "separators" they show that random separators are competitive baselines, having less than a 1% difference compared to previous self-optimisation methods. |
| Outcome: | The proposed method outperforms human-curated prompts in nine text classification tasks and eight language models and has a 40% chance of performing better than human-generated separators. |