Papers by Yihong Liu

G-LoRA: Global-Local Decoupled Low-Rank Adaptation (2026.findings-acl)

Challenge:	Existing model-based channel prediction methods suffer from limited accuracy due to imperfect temporal modeling, while existing AI-based methods suffers from limited generalization due to inadequate training strategies.
Approach:	They propose a generative pre-trained language model for channel prediction based on channel correlation and train it based upon transformer decoder architecture.
Outcome:	The proposed model can learn various channel characteristics and perform impressive tasks across multiple dimensions.

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Jiahao Xiong, Yihong Huang, Yihe Liu, Xianming Hu, Hongbo Zhao, Kai Zhang

Green & Sustainable NLP

Challenge:	Low-Rank Adaptation (LoRA) improves the fine-tuning efficiency and performance of large language models.
Approach:	They propose a low-rank adaptive approach that decomposes update matrix into global and local adapters and assigns them to local and global adapters.
Outcome:	The proposed method achieves up to 2.7% accuracy improvement over LoRA and its variants on commonsense reasoning, mathematical reasoning, and code generation.

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang

Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models (2025.acl-long)

Challenge:	Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach:	They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome:	The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.

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Mingyang Wang, Heike Adel, Lukas Lange, Yihong Liu, Ercong Nie, Jannik Strötgen, Hinrich Schuetze

Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation (2026.acl-long)

Challenge:	Multilingual language models store factual knowledge across languages but struggle to provide consistent responses to semantically equivalent prompts in different languages.
Approach:	They propose a linear shortcut method that bypasses computations in the final layers . this method improves accuracy and cross-lingual consistency .
Outcome:	The proposed method improves prediction accuracy and cross-lingual consistency.

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Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller, Vera Schmitt

Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners (2026.findings-acl)

Challenge:	Large language models excel at generating English counterfactuals but their effectiveness in generating multilingual counterfacts remains unclear.
Approach:	They conduct automatic evaluations on both directly generated and derived counterfactuals in six languages and find that cross-lingual perturbations follow common strategic principles.
Outcome:	The proposed models show that translation-based counterfactuals offer higher validity than their directly generated counterparts, but still fall short of matching the quality of the original English counterf actuals.

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Yihong Liu, Raoyuan Zhao, Hinrich Schuetze, Michael A. Hedderich

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors (2026.acl-long)

Challenge:	Recent work shows that large reasoning models arrive at the correct answer before completing textual reasoning steps, indicating the presence of latent reasoning.
Approach:	They conduct a systematic investigation of multilingual latent reasoning in large reasoning models across 11 languages.
Outcome:	The proposed model arrive at the correct answer before completing the reasoning steps, indicating the presence of latent reasoning.

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Raoyuan Zhao, Yihong Liu, Lena Altinger, Hinrich Schuetze, Michael A. Hedderich

How Programming Concepts and Neurons Are Shared in Code Language Models (2025.findings-acl)

Challenge:	Large language models (LLMs) are increasingly deployed in multilingual, real-world applications where user inputs introduce typographical errors.
Approach:	They propose a multilingual typo generation algorithm that simulates human-like errors based on language-specific keyboard layouts and typing behavior.
Outcome:	The proposed model can generate the correct answer ("500") under typos in English, German, and Russian.

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Amir Hossein Kargaran, Yihong Liu, François Yvon, Hinrich Schuetze

ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)

Challenge:	Several studies have focused on programming languages in a monolingual setting, but most focus on programming language models.
Approach:	They perform a few-shot translation task on 21 PL pairs using two Llama-based models and decode the embeddings of intermediate layers.
Outcome:	The proposed model assigns high probability to English tokens in the second half of the intermediate layers and language-specific neurons are concentrated in the bottom layers . the model's concept space is closer to English (including PL keywords) and the model is more efficient at identifying language-related neurons.

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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang

Structured Data in NLP

Challenge:	retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures .
Approach:	They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents.
Outcome:	The proposed method reduces offline indexing costs and accelerates retrieval.

Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding (2025.acl-srw)

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Qi Feng, Yihong Liu, Hinrich Schuetze

Flow-Adapter Architecture for Unsupervised Machine Translation (2022.acl-long)

Challenge:	Existing curriculum learning approaches rely on manually defined difficulty metrics which may not accurately reflect the model’s own perspective.
Approach:	They propose a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) they evaluate four datasets covering binary and multi-class classification tasks.
Outcome:	The proposed model leads to faster convergence and improved performance compared to standard random sampling.

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Yihong Liu, Haris Jabbar, Hinrich Schuetze

Responsible & Trustworthy NLP

Challenge:	Recent advances in deep learning have boosted the development of neural machine translation (NMT).
Approach:	They propose a flow-adapter architecture for unsupervised neural machine translation that leverages normalizing flows to model distributions of sentence-level latent representations.
Outcome:	The proposed model achieves competitive results on several unsupervised MT benchmarks.

M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)

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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank

The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)

Challenge:	Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research.
Approach:	They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text.
Outcome:	The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date.

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Yihong Tang, Kehai Chen, Xuefeng Bai, Zheng-Yu Niu, Bo Wang, Jie Liu, Min Zhang

How Transliterations Improve Crosslingual Alignment (2025.coling-main)

Challenge:	Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content.
Approach:	They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety.
Outcome:	The proposed method improves safety metrics while maintaining utility.

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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze

A Crosslingual Investigation of Conceptualization in 1335 Languages (2023.acl-long)

Challenge:	Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved.
Approach:	They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data.
Outcome:	The proposed approach improves crosslingual alignment even for random sentences.

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Yihong Liu, Haotian Ye, Leonie Weissweiler, Philipp Wicke, Renhao Pei, Robert Zangenfeind, Hinrich Schütze

SynthEval: Hybrid Behavioral Testing of NLP Models with Synthetic Evaluation (2024.findings-emnlp)

Challenge:	Conceptualizer is a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings.
Approach:	They propose a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings.
Outcome:	The proposed method has good alignment accuracy across all languages and on 32 Swadesh concepts.

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Raoyuan Zhao, Abdullatif Köksal, Yihong Liu, Leonie Weissweiler, Anna Korhonen, Hinrich Schuetze

Challenge:	Existing frameworks for benchmarking in NLP often overestimate performance . however, manually creating a variety of test types requires significant human labor .
Approach:	They propose a framework that leverages large language models to generate a wide range of test types . they first generate sentences via LLMs and then identifies challenging examples .
Outcome:	The proposed framework overestimates performance on two classification tasks.

OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining (2024.findings-naacl)

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Yihong Liu, Peiqin Lin, Mingyang Wang, Hinrich Schuetze

DialogBench: Evaluating LLMs as Human-like Dialogue Systems (2024.naacl-long)

Challenge:	Existing methods to pretrain multilingual models are limited by the number of embedding parameters and the complexity of the model.
Approach:	They propose a framework that initializes the embeddings of unseen subwords and can adapt a model to multiple languages.
Outcome:	The proposed framework can adapt a pre-trained model to multiple languages efficiently and effectively.

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Jiao Ou, Junda Lu, Che Liu, Yihong Tang, Fuzheng Zhang, Di Zhang, Kun Gai

TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data (2025.coling-main)

Challenge:	Existing benchmarks only evaluate LLMs' abilities for task completion as assistant AI.
Approach:	They propose a dialogue evaluation benchmark that contains 12 dialogue tasks to evaluate LLMs' capabilities as human-like dialogue systems.
Outcome:	The proposed benchmark contains 12 tasks to evaluate LLMs' capabilities . it shows that instruction tuning improves human likeness, but not as human-like systems .

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Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schütze

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)

Challenge:	Existing mPLMs that handle non-transliterated data are not sufficient to train crosslingual models.
Approach:	They propose a framework to transliterate related languages into a common script by exploiting existing mPLMs and their tokenizer without any training.
Outcome:	The proposed framework can create strong baselines for data that is transliterated into a common script by exploiting an existing mPLM and its tokenizer without any training.

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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang

Multimodality

Challenge:	TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites .
Approach:	They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs .
Outcome:	The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification .

A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages (2026.findings-eacl)

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Raoyuan Zhao, Yihong Liu, Hinrich Schuetze, Michael A. Hedderich

Low-Resource NLP

Challenge:	Recent work has examined final-answer accuracy in multilingual settings, but the behavior of thinking traces, i.e., the intermediate steps that lead to the final answer, remains underexplored.
Approach:	They propose to measure language compliance, answer accuracy, and answer consistency when LRMs are explicitly instructed or prompt-hacked to think in a target language.
Outcome:	The proposed model improves in English and other high-resource languages while relying on traces to varying degrees.

Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu (2025.acl-long)

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Renhao Pei, Yihong Liu, Peiqin Lin, François Yvon, Hinrich Schuetze

Machine Translation

Challenge:	In-context machine translation (MT) with large language models can take advantage of linguistic resources such as grammar books and dictionaries.
Approach:	They propose to use in-context machine translation (MT) with large language models to take advantage of linguistic resources such as grammar books and dictionaries.
Outcome:	The proposed approach can take advantage of dictionaries and grammar books, but its performance is poor for many lowresource languages.

You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)

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Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning for LLMs (2026.findings-acl)

Challenge:	Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers.
Approach:	They propose a transmitter-receiver framework which explicitly models understanding between interlocutors.
Outcome:	The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks.

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Huanyu Liu, Jia Li, Yihong Dong, Chang Yu, Taozhi Chen, Lecheng Wang, Yongding Tao, Bin Gu, Ge Li

ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)

Challenge:	Existing approaches to reinforcement learning with verifiable reward (RLVR) are limited by difficulty or lack of exploration.
Approach:	They propose a self-evolving curriculum learning framework based on chain-of-thought reasoning optimization that constrains exploration space by self-generating and verifying CoT trajectories.
Outcome:	The proposed framework enables LLMs to solve previously unsolved problems without external supervision and is compatible with various RL fine-tuning methods.

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Xu Wang, Bo Wang, Yihong Tang, Dongming Zhao, Jing Liu, Ruifang He, Yuexian Hou

LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains (2026.acl-long)

Challenge:	Empathy improves human-machine dialogue systems by enhancing the user's experience.
Approach:	They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder.
Outcome:	Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation.

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Weizhe Shi, Qiqi Wang, Yihong Pan, Qian Liu, Kaiqi Zhao

Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)

Challenge:	Current legalAI tasks divide sentencing and legal reasoning into two separate tasks, resulting in inconsistency between the reasoning and predictions.
Approach:	They propose a new task that generates both legal reasoning and sentencing decisions using a framework that applies structured legal chains to guide the model through comprehensive case assessments.
Outcome:	The proposed model outperforms baseline models on real-world, open-source Chinese legal case datasets.

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Yihong Dong, Yuchen Liu, Xue Jiang, Bin Gu, Zhi Jin, Ge Li

Programming Languages in NLP

Challenge:	Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation.
Approach:	They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions.
Outcome:	The proposed approach outperforms baselines and humanEval benchmarks on CodeRepetEval dataset and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.

RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems (2025.coling-main)

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Yihong Tang, Bo Wang, Xu Wang, Dongming Zhao, Jing Liu, Ruifang He, Yuexian Hou

Responsible & Trustworthy NLP

Challenge:	Existing approaches to combat character hallucination are vulnerable to attack . large language models (LLMs) are capable of generating responses inconsistent with intended personas .
Approach:	They propose a novel defence strategy that generates supplemental context through narration to mitigate role-query conflicts and improve query generalization.
Outcome:	The proposed defence strategy outperforms refusal-based strategies in character hallucinations and query generalization.

TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models (2024.acl-long)

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Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schuetze

Experience-Driven Reflective Co-Evolution of Prompts and Heuristics for Autonomous Algorithm Design (2026.findings-acl)

Challenge:	The world’s more than 7000 languages are written in at least 293 scripts, which poses a difficulty for multilingual pretrained language models in learning crosslingual knowledge through lexical overlap.
Approach:	They propose a framework that optimizes the Transliteration Contrastive Modeling objective to fine-tune an mPLM by contrasting sentences in its training data and transliterations in a unified script.
Outcome:	The proposed model outperforms Glot500-m on zero-shot crosslingual transfer tasks while retaining uniformity across scripts.

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Yihong Liu, Junyi Li, Hongyu Lu, Xin Zhao, Ji-Rong Wen

On Relation-Specific Neurons in Large Language Models (2025.emnlp-main)

Challenge:	Combinatorial optimization has long been dominated by manually engineered heuristics, which require substantial expert intuition and implementation overhead.
Approach:	They propose a framework that couples an island migration model with elite selection to maintain population diversity.
Outcome:	The proposed framework achieves superior accuracy on the Traveling Salesman and Bin Packing Problems.

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Yihong Liu, Runsheng Chen, Lea Hirlimann, Ahmad Dawar Hakimi, Mingyang Wang, Amir Hossein Kargaran, Sascha Rothe, François Yvon, Hinrich Schuetze

HYPEROFA: Expanding LLM Vocabulary to New Languages via Hypernetwork-Based Embedding Initialization (2025.acl-srw)

Challenge:	In large language models, certain neurons can store distinct pieces of knowledge learned during pretraining.
Approach:	They hypothesize that relation-specific neurons detect relation in input text and guide generation involving such a relation.
Outcome:	The proposed model can handle facts involving relation r and facts containing a different relation .

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Enes Özeren, Yihong Liu, Hinrich Schuetze

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)

Challenge:	Pre-trained language models exhibit suboptimal performance on mid- and low-resource languages due to limited exposure to these languages during pre-training.
Approach:	They propose a similarity-based subword embedding initialization heuristic that introduces new tokens specific to target languages, initializes their embedders, and applies continual pre-training on target-language data.
Outcome:	The proposed method outperforms random initialization baseline and matches or exceeds OFA in both continual pre-training convergence and downstream task performance.

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Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li

LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)

Challenge:	Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data.
Approach:	They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution.
Outcome:	The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution.

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Yihong Liu, Haotian Ye, Chunlan Ma, Mingyang Wang, Hinrich Schuetze

DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)

Challenge:	Recent multilingual pretrained language models often avoid using language embeddings, which places a significant burden on token representations to encode all language-specific information.
Approach:	They propose a method that incorporates both language and script embeddings into the output of Transformer blocks before passing the final representations to the language modeling head for prediction.
Outcome:	The proposed method outperforms the baseline model in zero-shot crosslingual transfer across diverse downstream tasks.

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Senbo Zhang, Qiqi Wang, Fanghao Lou, Guanyu Chen, Yihong Pan, Huijia Li, Qian Liu

Challenge:	Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning.
Approach:	They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims.
Outcome:	The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches.

Tracing Multilingual Factual Knowledge Acquisition in Pretraining (2025.findings-emnlp)

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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Felicia Körner, Ercong Nie, Barbara Plank, François Yvon, Hinrich Schuetze

Language Modeling with Sparse Product of Sememe Experts (D18-1)

Challenge:	Large Language Models are capable of recalling multilingual factual knowledge, but most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency unexplored.
Approach:	They trace how factual recall and crosslingual consistency evolve during pretraining, focusing on OLMo-7B as a case study.
Outcome:	The results show that fact frequency is the key to a better recall of multilingual facts, regardless of language, and some low-frequency facts in non-English languages can still be correctly recalled.

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Yihong Gu, Jun Yan, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)

Challenge:	Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words.
Approach:	They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics .
Outcome:	Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM.

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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao

Challenge:	High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly.
Approach:	They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth.
Outcome:	the proposed pipeline outperforms 14 leading baselines on 16 benchmarks.

CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)

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Kechi Zhang, Ge Li, Yihong Dong, Jingjing Xu, Jun Zhang, Jing Su, Yongfei Liu, Zhi Jin

Programming Languages in NLP

Challenge:	Existing training methods for code generation do not improve code correctness and efficiency.
Approach:	They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency.
Outcome:	The proposed framework improves code correctness and efficiency by integrating preference learning into code generation.

Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs (2023.findings-emnlp)

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Yihong Liu, Haotian Ye, Leonie Weissweiler, Renhao Pei, Hinrich Schuetze

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)

Challenge:	Existing work on colexification patterns relies on annotated word lists, limiting scalability and usefulness in NLP.
Approach:	They propose two methods to train multilingual graphs from colexification patterns using an unannotated parallel corpus.
Outcome:	The proposed methods achieve high recall on CLICS and transfer learning in multilingual graphs.

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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li

Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment (2024.findings-emnlp)

Challenge:	Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities.
Outcome:	The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks.

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Orgest Xhelili, Yihong Liu, Hinrich Schuetze