publications

2026

1. 

   HARDTESTGEN: A High-Quality RL Verifier Generation Pipeline for LLM Algorithimic Coding

   Zhongmou He^* , Yee Man Choi^* , Kexun Zhang^*, and 7 more authors

   In The Fourteenth International Conference on Learning Representations, 2026

2. 

   CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation

   Yee Man Choi , Xuehang Guo , Yi R. Fung, and 1 more author

   In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2026

   Abs

   Large Language Models (LLMs) have emerged as powerful assistants for scientific writing. However, concerns remain about the quality and reliability of the generated text, including citation accuracy and faithfulness. While most recent work relies on methods such as LLM-as-a-Judge, the reliability of LLM-as-a-Judge alone is also in doubt. In this work, we reframe citation evaluation as a problem of citation attribution alignment, which assesses whether LLM-generated citations match those a human author would include for the same text. We propose CiteGuard, a retrieval-aware agent framework designed to provide more faithful grounding for citation validation. CiteGuard improves over the prior baseline by 10 percentage points and achieves up to 68.1% accuracy on the CiteME benchmark, approaching human performance (69.2%). It also identifies alternative valid citations and demonstrates generalization ability for cross-domain citation attribution.

3. 

   The Illusion of Choice: Assessing Impact of Manipulated LLMs

   Yee Man Choi , Abdul Basit Ali , Krzysztof Czarnecki, and 1 more author

   In ICML Workshop on Technical AI Governance Research, 2026
4. Where Does the Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLMs for Autonomous Driving

   Yimu Wang , Yee Man Choi , Barry Zhang, and 3 more authors

   2026
5. LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering

   Xuye Liu , Yimu Wang , Peng Shi, and 7 more authors

   2026
6. ATTRICITE: Training an Open 4B Model for Citation Recovery toward Faithful Attribution

   Yee Man Choi , Xuehang Guo , Songcheng Cai, and 3 more authors

   2026

2024

1. 

   Hire a Linguist!: Learning Endangered Languages in LLMs with In-Context Linguistic Descriptions

   Kexun Zhang , Yee Man Choi , Zhenqiao Song, and 3 more authors

   In Findings of the Association for Computational Linguistics: ACL 2024, Aug 2024

   Abs DOI

   How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LingoLLM, a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. Our key insight is to demonstrate linguistic knowledge of an unseen language in an LLM’s prompt, including a dictionary, a grammar book, and morphologically analyzed input text. We implement LingoLLM on top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks across 8 endangered or low-resource languages. Our results show that LingoLLM elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions. Our findings demonstrate the tremendous value of linguistic knowledge in the age of LLMs for endangered languages. Our data, code, and model generations will be released to the public. Our data, code, and model generations can be found at \urlhttps://github.com/LLiLab/llm4endangeredlang.