Papers by Chunhua Liu
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models (2025.findings-acl)
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| Challenge: | State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks such as revising source code to address code reviews. |
| Approach: | They propose a benchmark to evaluate large language models' ability to bridge both technical and conversational contexts by decomposing the generation task of code refinement into three essential reasoning steps. |
| Outcome: | The proposed benchmark exposes specific model weaknesses in code review comprehension disentangled from their generative automated code refinement results. |
WAX: A New Dataset for Word Association eXplanations (2022.aacl-main)
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| Challenge: | Word associations are among the most common paradigms to study the human mental lexicon. |
| Approach: | They present a large dataset of word associations with explanations and relation labels . they show that current language models struggle to capture the diversity of human associations . |
| Outcome: | The proposed model fails to capture the diversity of human associations, the authors show . they show that the model is a rich benchmark for commonsense modeling and generation. |
ALIGN: Word Association Learning for Cultural Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Large language models exhibit cultural bias from over-represented viewpoints in training data, yet cultural alignment remains a challenge due to limited cultural knowledge and a lack of exploration into effective learning approaches. |
| Approach: | They propose a cost-efficient method for fine-tuning large language models on native speakers’ word-association norms and a preference optimization method to improve cultural alignment. |
| Outcome: | The proposed model trains Llama-3.1-8B and Qwen-2.5-7B on native speakers’ word-association norms and shows that such associations capture cultural knowledge. |
REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking (2025.emnlp-main)
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| Challenge: | Existing LLMs face ranking uncertainty, unstable top-k recovery, and high token cost due to token-intensive prompting. |
| Approach: | They propose a re-ranking framework that captures uncertainty and refines LLM-derived relevance through recursive Bayesian updates. |
| Outcome: | The proposed framework outperforms state-of-the-art re-rankers while reducing token usage and latency. |
Comparing Moral Values in Western English-speaking societies and LLMs with Word Associations (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are trained on extensive corpora to learn linguistic patterns, contextual nuances, and implicit elements of human values. |
| Approach: | They propose to use word associations as low-level underlying representations to obtain a more robust picture of LLMs’ moral reasoning. |
| Outcome: | The proposed method reveals detailed but systematic differences between LLMs and human associations. |
Seeking Clozure: Robust Hypernym extraction from BERT with Anchored Prompts (2023.starsem-1)
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| Challenge: | Existing methods for extracting hypernym knowledge from large language models are unclear whether they fail due to a lack of knowledge or shortcomings. |
| Approach: | They propose to use pattern-based hypernym extraction as a diagnostic tool to examine hypernomy knowledge encoded in BERT. |
| Outcome: | The proposed method compares the results of two different methods on six English data sets and on challenge sets of rare and abstract concepts. |
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations (D19-60)
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| Challenge: | Experimental results show that our system achieves significant improvements over the baseline systems with 84.2% accuracy on the official test dataset. |
| Approach: | They propose a system to inject more external knowledge into everyday narrations . they use a pre-trained BERT model to fine-tune on a machine reading comprehension dataset . |
| Outcome: | The proposed system achieves significant improvements over baseline systems with 84.2% accuracy on the official test dataset. |