Papers by Yanshuai Cao
TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface (2021.acl-demo)
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Peng Xu, Wenjie Zi, Hamidreza Shahidi, Ákos Kádár, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, Yanshuai Cao
| Challenge: | Existing text-to-SQL semantic parsers cannot achieve high accuracy in cross-database setting . TURING is a NLDB system that can be used to democratize data-driven insights for non-technical users . |
| Approach: | They propose a TURING system that provides high-precision natural language explanations of SQL queries in a beam. |
| Outcome: | The proposed system achieves 75.1% execution accuracy and 78.3% top-5 beam execution accuracy on the Spider validation set. |
Optimizing Deeper Transformers on Small Datasets (2021.acl-long)
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Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao
| Challenge: | a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets. |
| Approach: | They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch. |
| Outcome: | The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch . |
Jump Starting Bandits with LLM-Generated Prior Knowledge (2024.emnlp-main)
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| Challenge: | Contextual multi-armed bandits generate personalized recommendations based on user-specific contexts. |
| Approach: | They propose an initialization algorithm for contextual bandits by prompting LLMs to produce a pre-training dataset of approximate human preferences for the bandit. |
| Outcome: | The proposed approach significantly reduces online learning regret and data-gathering costs for training such models. |
A Cross-Domain Transferable Neural Coherence Model (P19-1)
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Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung
| Challenge: | Existing coherence models do not generalize to unseen categories of text . previous work advocates for generative models for cross-domain generalization . |
| Approach: | They propose a local discriminative neural model with a smaller negative sampling space that can discriminate against incorrect orderings. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a standard benchmark dataset on the Wall Street Journal corpus and multiple challenging settings on Wikipedia articles. |
Adversarial Contrastive Estimation (P18-1)
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| Challenge: | Noise contrastive estimation (NCE) is a general strategy used in word embeddings and translations for knowledge graphs. |
| Approach: | They propose to augment negative sampler into mixture distribution with adversarially learned sampler and to combine it with noise contrastive estimation (NCE) they observe faster convergence and improved results on multiple metrics. |
| Outcome: | The proposed model performs better on word embeddings, order embedds and knowledge graph embeddments and faster convergence and improved results on multiple metrics. |
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are often evaluated on math word problems . however, such metrics conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Approach: | They propose to use Final-answer-based metrics to evaluate large language models on math word problems to conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Outcome: | The proposed model performance is bottlenecked by arithmetic computation and not abstract formulation, the study shows. |
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data (2021.acl-short)
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| Challenge: | a generic transformer-based model can achieve competitive performance with minimal code-generation-specific inductive bias design. |
| Approach: | They investigate whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design. |
| Outcome: | The proposed model achieves 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa. |