Papers by Tianjian Li
Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution (2023.findings-acl)
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| Challenge: | Existing studies on cross-lingual transferability of multilingual LMs show that they can perform tasks in low-resource languages. |
| Approach: | They propose a method to regularize the model from learning language invariant representations and a way to select model checkpoints without a development set in the target language. |
| Outcome: | The proposed method reduces the accidental translation problem by 68% and improves the ROUGE-L score by 1.5 on average. |
Benchmarking Language Model Creativity: A Case Study on Code Generation (2025.naacl-long)
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| Challenge: | Recent studies on LLM creativity evaluation focus on open-ended generation tasks . however, the degree to which LLMs possess and utilize creativity for problem-solving remains unclear . |
| Approach: | They propose a framework for quantifying LLM creativity that incorporates design ingredients . they introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions . |
| Outcome: | The proposed framework quantifies creativity in LLMs on Codeforces problems . it also finds that even the most creative model fails to demonstrate human-like creativity . |
Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data (2025.naacl-long)
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| Challenge: | Recent efforts to verify text accuracy provide no guarantees on their correctness . a new method to improve LLMs' verifiability is to use quotes to ground models . |
| Approach: | They propose a method that allows models to quote verbatim statements from trusted sources . they leverage a fast membership inference function to verify text against trusted corpora . |
| Outcome: | The proposed method significantly increases verbatim quotes from high-quality documents by up to 130% relative to base models while maintaining response quality. |
Upsample or Upweight? Balanced Training on Heavily Imbalanced Datasets (2025.naacl-long)
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| Challenge: | a lack of data across domains creates significant imbalances in training data sizes . a recent study shows that temperature sampling and scaling are equivalent but differ under stochastic gradient descent due to differences in gradient variance. |
| Approach: | They propose a method that upsamples low-resource languages and upweights their loss functions to address this disparity. |
| Outcome: | The proposed method competes effectively with existing data re-weighting techniques while offering computational efficiency. |