Papers by Hai Hu
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have made great progress in text generation but suffer from hallucinations during reasoning and generation. |
| Approach: | They propose an inference-time method to help LLMs decode truthfully by selecting tokens with the lowest probabilities and concatenating them to the original context. |
| Outcome: | The proposed method improves LLaMA-7b, LLama2-7b and Mistral-7b on hallucination tasks. |
PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to reduce memory usage for large language models neglect inter-layer dependency between layers and huge memory consumption in pre-computation. |
| Approach: | They propose a method that compresses the KV cache by layer-wise retaining crucial context. |
| Outcome: | The proposed method reduces memory usage by layer-wise retaining crucial context . it can improve 2.2x throughput compared to Accelerate with over 54% memory reduction . |
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
MELA: Multilingual Evaluation of Linguistic Acceptability (2024.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks on linguistic acceptability have been used to evaluate language models' ability to distinguish between acceptable and unacceptable sentences. |
| Approach: | They present the largest benchmark to date on linguistic acceptability: MELA . they establish LLM baselines on this benchmark and investigate cross-lingual transfer in acceptability judgements with XLM-R. |
| Outcome: | The proposed model outperforms open-source models on cross-lingual transfer in acceptability judgements. |
Automatic Article Commenting: the Task and Dataset (P18-2)
Copied to clipboard
| Challenge: | Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums. |
| Approach: | They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics. |
| Outcome: | The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
Copied to clipboard
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)
Copied to clipboard
Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu
| Challenge: | Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters. |
| Approach: | They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases. |
| Outcome: | The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters . |
OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
| Approach: | They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations. |
| Outcome: | The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics. |
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to extract aspects and opinions independently, optionally adding pairwise relations, often lead to error propagation and high time complexity. |
| Approach: | They propose a transition-based model that performs aspect and opinion extraction jointly and integrates contrastive-augmented optimization. |
| Outcome: | The proposed model outperforms previous models on two out of four datasets when trained on a single dataset. |
Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)
Copied to clipboard
Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu
| Challenge: | Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts. |
| Approach: | They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments. |
| Outcome: | The proposed measure can generalize to unseen genres, authors, and language pairs. |
VHASR: A Multimodal Speech Recognition System With Vision Hotwords (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that incorporate audio-related image information do not improve speech recognition performance. |
| Approach: | They propose a novel approach utilizing audio-related image information and set up a multimodal speech recognition system that uses vision as hotwords to enhance the model’s speech recognition capability. |
| Outcome: | The proposed model outperforms unimodal ASR model and achieves SOTA among existing image-based multimodal ASL models. |
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data (2026.eacl-long)
Copied to clipboard
Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck, Bhargav Shandilya, Diana Galvan-Sosa, Faiz Ghifari Haznitrama, Francesca Padovani, Francois Meyer, Hai Hu, Julen Etxaniz, Laurent Prevot, Linyang He, María Grandury, Mila Marcheva, Negar Foroutan, Nikitas Theodoropoulos, Pouya Sadeghi, Siyuan Song, Suchir Salhan, Susana Zhou, Yurii Paniv, Ziyin Zhang, Arianna Bisazza, Alex Warstadt, Leshem Choshen
| Challenge: | prevailing trend in language modeling research is to prioritize scaling, authors say . from infancy to maturity, English learners acquire language through exposure to less than 100M words . |
| Approach: | They propose a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. |
| Outcome: | The proposed models outperform models trained on a fixed, developmentally plausible English corpus on various benchmarks. |