Papers by Le Zhang
De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Named entity recognition (NER) rely on labeled data, which is labor-intensive. |
| Approach: | They propose a method to de-biase DS-NER models by a structural Causal Model . they propose to use a causal invariance regularizer to make them more robust . |
| Outcome: | The proposed method significantly improves DS-NER models on four datasets and three DS NER models. |
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search (2025.acl-long)
Copied to clipboard
Yize Zhang, Tianshu Wang, Sirui Chen, Kun Wang, Xingyu Zeng, Hongyu Lin, Xianpei Han, Le Sun, Chaochao Lu
| Challenge: | Large language models (LLMs) have impressive capabilities but their application in open-ended, knowledge-intensive, complex reasoning scenarios is limited. |
| Approach: | They propose a framework that integrates risk assessment of intermediate reasoning states with dynamic retrieval-augmented generation within a Monte Carlo tree search paradigm. |
| Outcome: | The proposed framework outperforms the state-of-the-art KAR methods by up to 23.10% and the latest RAG-equipped large reasoning models by upto 25.37%. |
All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)
Copied to clipboard
Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun
| Challenge: | Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language. |
| Approach: | They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking. |
| Outcome: | The proposed approach mitigates language bias and consistently improves mRAG performance across languages. |
Probing Simile Knowledge from Pre-trained Language Models (2022.acl-long)
Copied to clipboard
Weijie Chen, Yongzhu Chang, Rongsheng Zhang, Jiashu Pu, Guandan Chen, Le Zhang, Yadong Xi, Yijiang Chen, Chang Su
| Challenge: | Existing approaches to learn generic knowledge from a large corpus are time-consuming and labor-intensive. |
| Approach: | They propose a framework to probe simile knowledge from pre-trained language models to solve SI and SG tasks. |
| Outcome: | The proposed framework solves the SI and SG tasks in a simile triple completion task. |
MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)
Copied to clipboard
Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han
| Challenge: | Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead. |
| Approach: | They propose an agent framework that maintains a compact memory during multi-turn interactions. |
| Outcome: | The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions. |
Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)
Copied to clipboard
Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang
| Challenge: | Recent studies have focused on a single pass of lyrics generation with little human intervention. |
| Approach: | They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes. |
| Outcome: | The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly. |
DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)
Copied to clipboard
Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing presentation agents rely on predefined workflows and fixed templates to generate presentations. |
| Approach: | They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation. |
| Outcome: | The proposed framework can be used to generate presentations with environmental observations. |
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training (2024.acl-long)
Copied to clipboard
Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Jianan Zhao, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang
| Challenge: | Experimental results demonstrate that ProtLLM achieves superior performance against protein-specialized baselines on protein-centric tasks and induces zero-shot and in-context learning capabilities on protein language tasks. |
| Approach: | They propose a cross-modal large language model (LLM) that can handle protein-centric and protein-language tasks by using a dynamic protein mounting mechanism. |
| Outcome: | The proposed model can predict proteins from a vast pool of candidates and can also predict natural language and biological papers. |
Exploring the Best Practices of Query Expansion with Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are foundational in language technologies, particularly in information retrieval (IR). |
| Approach: | They propose a framework that leverages large language models for query expansion . they use LLMs to generate multiple pseudo-references and integrate them with original queries . |
| Outcome: | The proposed framework enhances sparse and dense retrieval methods without pre-indexing. |
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)
Copied to clipboard
Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai
| Challenge: | Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets. |
| Approach: | They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding. |
| Outcome: | The proposed method can solve the semantic gap and structure gap on multiple datasets. |
Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)
Copied to clipboard
Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities. |
| Approach: | They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation. |
| Outcome: | The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement. |
DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)
Copied to clipboard
| Challenge: | Open-domain question answering is a task that requires answering questions based on a collection of document images. |
| Approach: | They propose to use document images to answer questions using layouts and visual features instead of text. |
| Outcome: | The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features. |
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)
Copied to clipboard
Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun
| Challenge: | Existing benchmarks focus on character-centric approach and fail to reflect real-world applications. |
| Approach: | RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds. |
| Outcome: | RMTBench features 80 diverse characters and over 8,000 dialogue rounds. |
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) like GPT-4 are not able to handle multi-modal open-domain question answering in a zero-shot manner. |
| Approach: | MoqaGPT uses divide-and-conquer strategy to extract answers from each modality separately. |
| Outcome: | MoqaGPT improves on MMCoQA dataset by +37.91 points and EM by +34.07 points. |
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)
Copied to clipboard
Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen
| Challenge: | Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society. |
| Approach: | They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system . |
| Outcome: | The proposed system simulates trending topics under poisoning attacks on social media platforms. |
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)
Copied to clipboard
Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao
| Challenge: | Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools. |
| Approach: | They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment. |
| Outcome: | The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool. |
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing KG-augmented models for commonsense question answering ignore the effectively fusing and reasoning over question context representations and the KG representations. |
| Approach: | They propose a novel model which combines a logical reasoning and a dynamic pruning mechanism to solve these limitations. |
| Outcome: | The proposed model improves existing models and performs interpretable reasoning on the CommonsenseQA and OpenBookQA datasets. |
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)
Copied to clipboard
Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang
| Challenge: | Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed . |
| Approach: | They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision. |
| Outcome: | The proposed framework reduces token usage while improving accuracy on math benchmarks. |
Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026.acl-long)
Copied to clipboard
Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, Junyang Lin
| Challenge: | Recent studies observe a phenomenon where reward models achieve high accuracy on static datasets but fail to generalize effectively during RLHF. |
| Approach: | They propose a method that combines rationale consistency with outcome accuracy to improve performance on RM-Bench and JudgeBench. |
| Outcome: | The proposed method surpasses baselines on RM-Bench and JudgeBench by an average of 5% and improves creative writing tasks by 7%. |
Across Programming Language Silos: A Study on Cross-Lingual Retrieval-Augmented Code Generation (2026.findings-acl)
Copied to clipboard
Qiming Zhu, Jialun Cao, Xuanang Chen, Weili Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung
| Challenge: | Current research on large language models with retrieval-augmented code generation (RACG) has focused on single-language settings, leaving their cross-lingual effectiveness underexplored. |
| Approach: | They construct a dataset covering 13 PLs with nearly 14K instances to study cross-lingual code knowledge transfer in RACG. |
| Outcome: | The proposed model shows unequal cross-lingual knowledge transfer even with direct injection and shows limited reliance on natural language information embedded in code when equipped with a code-specific retriever. |
Sample Efficient Alignment Learning With Episodic Control (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing parametric methods for aligning large language models with task objectives are limited. |
| Approach: | They propose a non-parametric framework that aligns large language models with task objectives . they use a key-value memory to store associations between generated text and its corresponding values . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on harmless, helpful, and summarization tasks. |
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)
Copied to clipboard
Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. |
| Approach: | They propose a framework that allows large language models to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
| Outcome: | The proposed framework enables LLMs to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Scientific discovery evolution does not occur ex nihilo but is characterized by structural deepening and reconfiguration of existing functionalities. |
| Approach: | They propose a framework for hypothesis generation based on evolutionary narratives . they extract structured P-M-L-F quadruples from citation networks and introduce a mechanism to assess their semantic compatibility. |
| Outcome: | The proposed framework reduces logical disconnects by evaluating its semantic compatibility. |
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input (2022.acl-demo)
Copied to clipboard
| Challenge: | Existing systems based on attributes or keywords render lyrics generation very limited . previous studies focused on generating lyrics based only on attributes and keywords . |
| Approach: | They propose to use Chinese passage-level text as input for lyrics generation . they initialize parameters with custom pretrained Chinese GPT-2 model and adopt a two-step process to fine-tune the model for better alignment between passage- level text and lyrics. |
| Outcome: | The proposed system is conditioned on passage-level text rather than attributes or keywords, rendering limited control over the content of the lyrics. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning (2025.acl-long)
Copied to clipboard
Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le, Zehong Wang, Tianyi Ma, Vincent Galassi, Keerthiram Murugesan, Nuno Moniz, Werner Geyer, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
| Challenge: | Diet plays a critical role in human health, but tailoring dietary reasoning to individual health conditions remains a challenge. |
| Approach: | a new benchmark evaluates dietary reasoning using a national health survey data set. |
| Outcome: | The NGQA benchmark evaluates dietary reasoning across three tasks using a set of question complexity settings and baseline models. |
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (2022.emnlp-main)
Copied to clipboard
| Challenge: | Our proposed method extracts N-ary relation tuples from scientific articles. |
| Approach: | They propose a method that decomposes the task into two stages . they propose modal query and modal entity selection . their results show that ReSel outperforms state-of-the-art baselines significantly . |
| Outcome: | The proposed method outperforms state-of-the-art baselines on three scientific information extraction datasets. |
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models (2026.findings-acl)
Copied to clipboard
Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing methods often apply coarse-grained constraints over entire reasoning trajectories . Existing approaches often apply unsafe constraints, causing unsafe outputs . |
| Approach: | They propose a trajectory-level training framework that mitigates Self-Jailbreak . they propose 'chain-of-guardrail' to mitigate self-jailbreak by targeting step-level interventions . |
| Outcome: | The proposed framework mitigates Self-Jailbreak by targeting step-level interventions while maintaining reasoning ability. |
REARANK: Reasoning Re-ranking Agent via Reinforcement Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | REARANK is a large language model-based listwise reasoning reranking agent . it explicitly reasons be- fore reranked results, significantly improving performance and interpretability. |
| Approach: | They propose a large language model-based listwise reasoning reranking agent that explicitly reasons be- fore reranked lists. |
| Outcome: | The proposed agent outperforms GPT-4 on reasoning-intensive benchmarks and surpasses GPL-4 on BRIGHT benchmarks. |
All Information is Valuable: Question Matching over Full Information Transmission Network (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for question matching only transmit one kind of information while failing to utilize both kinds of information simultaneously. |
| Approach: | They propose a question matching network that can transmit both representation and interactive information together in a simultaneous fashion. |
| Outcome: | The proposed approach outperforms strong baseline models on two standard benchmarks. |
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition (2021.acl-long)
Copied to clipboard
| Challenge: | Existing NER models are supervised by a large number of training sequences, each pre-annotated with token-level labels. |
| Approach: | They propose a conditional hidden Markov model which can effectively infer true labels from multi-source noisy labels in an unsupervised way. |
| Outcome: | The proposed model outperforms state-of-the-art weakly supervised NER models on four benchmarks from various domains. |
Language Modeling with Shared Grammar (P19-1)
Copied to clipboard
| Challenge: | Recent work on structure-aware models have shown promising results on language modeling, but how to incorporate structure knowledge on corpus without syntactic annotations remains an open problem. |
| Approach: | They propose a neural variational language model which enables the sharing of grammar knowledge among different corpora. |
| Outcome: | The proposed model converges significantly faster to lower perplexity on two popular benchmark datasets. |
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on acquiring relevant knowledge by retrieving external knowledge bases and fine-tuning pre-trained models. |
| Approach: | They propose a two-stage prompt-based unsupervised commonsense question answering framework that leverages implicit knowledge stored in PrLMs to generate knowledge for questions with unlimited types and possible candidate answers independent of specified choices. |
| Outcome: | The proposed framework significantly improves the reasoning ability of language models in unsupervised settings. |
MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have revolutionized various domains, offering unprecedented performance across numerous tasks. |
| Approach: | They propose a new Mixture of Low-Rank Experts (MoRE) for multi-task PEFT to improve performance of LLMs with fewer parameters. |
| Outcome: | The proposed method improves performance over multiple tasks and no additional inference cost. |
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing data augmentation methods miss the important characteristic of compositionality, meaning of a complex expression is built from its sub-parts. |
| Approach: | They propose a compositional data augmentation approach for natural language understanding called TreeMix that leverages constituency parsing tree to decompose sentences into constituent sub-structures and the Mixup data enhancing technique to recombine them to generate new sentences. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on text classification and SCAN. |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
Copied to clipboard
Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)
Copied to clipboard
Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han
| Challenge: | Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. |
| Approach: | They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction. |
| Outcome: | The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. |
Distilling Text Style Transfer With Self-Explanation From LLMs (2024.naacl-srw)
Copied to clipboard
| Challenge: | Text Style Transfer (TST) aims to alter the style of text while preserving its core content. |
| Approach: | They propose a framework that leverages large language models alongside chain-of-thought prompting to facilitate TST. |
| Outcome: | The proposed framework surpasses supervised fine-tuning and knowledge distillation methods in low-resource settings. |
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)
Copied to clipboard
Hao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
| Challenge: | Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence. |
| Approach: | They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides. |
| Outcome: | The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides . |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |
Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics (2023.emnlp-demo)
Copied to clipboard
| Challenge: | Existing studies on lyrics generation focus on generating accurate lyrics using keywords, rhymes, etc. However, there is no parallel corpus for lyrics imitation. |
| Approach: | They propose a Chinese lyrics imitation system that can generate new lyrics based on source lyrics. |
| Outcome: | The proposed system can generate new lyrics based on the source lyrics . human evaluation shows it can perform better lyric imitation. |
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manual designing a comprehensive, high-quality set of labeling rules is tedious and difficult. |
| Approach: | They propose a weakly-supervised learning model that iterates and discovers new labeling rules from data to improve the WSL model. |
| Outcome: | The proposed model outperforms state-of-the-art models on four tasks and bridges the gaps with fully supervised models. |
Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)
Copied to clipboard
| Challenge: | Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks. |
| Approach: | They propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution. |
| Outcome: | The proposed framework enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution. |
Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch (2025.acl-long)
Copied to clipboard
Xueru Wen, Jie Lou, Zichao Li, Yaojie Lu, XingYu XingYu, Yuqiu Ji, Guohai Xu, Hongyu Lin, Ben He, Xianpei Han, Le Sun, Debing Zhang
| Challenge: | Existing Chinese resources are small in scale and limited to specific domains, making them insufficient for LLM post-training. |
| Approach: | They propose a Chinese-annotated reward model and a preference dataset to address this gap . they evaluate Chinese RMs on CheemsBench and construct an RM that captures human preferences . |
| Outcome: | The proposed RM achieves state-of-the-art performance on CheemsBench and CheeMePreference. |
From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs (2025.acl-long)
Copied to clipboard
Jialun Cao, Yaojie Lu, Meiziniu Li, Haoyang Ma, Haokun Li, Mengda He, Cheng Wen, Le Sun, Hongyu Zhang, Shengchao Qin, Shing-Chi Cheung, Cong Tian
| Challenge: | Recent studies in formal mathematical reasoning have shown an unstoppable growth trend. |
| Approach: | They constructed 18k high-quality instruction-response pairs across five mainstream formal specification languages and evaluated them against ten open-sourced LLMs. |
| Outcome: | The proposed model compared instruction-response pairs across five formal specification languages and found that the LLMs were good at writing proof segments when given either the code, or the detailed description of proof steps. |
Improving Multi-Agent Debate with Sparse Communication Topology (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to multi-agent debates use a brute force algorithm, resulting in a computationally intensive process. |
| Approach: | They propose to extend the multi-agent debate framework to multi-modal reasoning and alignment labeling tasks, showcasing its broad applicability and effectiveness. |
| Outcome: | The proposed framework can achieve comparable or superior performance while significantly reducing computational costs. |
FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing faithful RAG approaches enforce strict context adherence, but they forcibly suppress the model’s parametric knowledge, which undermines the model's internal knowledge structure and increases the risk of misinterpreting the context. |
| Approach: | They propose a framework that resolves knowledge conflicts by explicitly modeling discrepancies between the model’s parametric knowledge and retrieved context. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in knowledge conflict cases and identifies conflicting knowledge at the fact level and designs a self-thinking process. |
Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression (2026.findings-acl)
Copied to clipboard
Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, Yanmin Qian
| Challenge: | Existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. |
| Approach: | They propose to use a large-scale, context-aware speech corpus derived from multi-speaker audiobooks to generate a speech that is human-like. |
| Outcome: | The proposed model outperforms existing methods in terms of emotional expression accuracy and naturalness. |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |
StitchLLM: Serving LLMs, One Block at a Time (2025.acl-long)
Copied to clipboard
Bodun Hu, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella
| Challenge: | Existing techniques like distillation and pruning are not efficient for large language models. |
| Approach: | They propose a dynamic model routing framework that uses a powerful bottom model to process all queries and a lightweight routing mechanism to allocate computational resources appropriately. |
| Outcome: | The proposed framework improves system throughput while minimizing performance degradation. |
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation (2024.findings-acl)
Copied to clipboard
| Challenge: | Current evaluations for large language models use a single-item assessment paradigm . current evaluations struggle to discern whether a model possesses the required capabilities or merely memorizes/guesses the answers to specific questions. |
| Approach: | They propose a framework to evaluate large language models using atomic test objectives. |
| Outcome: | The proposed evaluation framework resists data contamination and reduces interference of potential biases, and sheds light on the design of future principled and trustworthy LLM evaluation protocols. |
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to persona simulation large language models (LLMs) focus on learning basic biographical information, or using limited role-play dialogue datasets to capture a character’s responses. |
| Approach: | They propose to train characters using a linguistic structure and a style-tuning mechanism that allows a general linguistic style expert to collaborate with other task-specific experts to better understand their thoughts. |
| Outcome: | The proposed model outperforms baselines on linguistic accuracy and opinion comprehension on three tasks for Lu Xun's essay collection. |
ReasonerRank: Redefining Language Model Evaluation with Ground-Truth-Free Ranking Frameworks (2025.findings-acl)
Copied to clipboard
Jiamu Zhang, Jiayi Yuan, Andrew Wen, Hoang Anh Duy Le, Yu-Neng Chuang, Soo-Hyun Choi, Rui Chen, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly adopted across real-world applications . traditional evaluations rely on expensive, domain-specific ground-truth labels . obtaining labeled data is expensive, time-consuming, and often requires domain expertise . |
| Approach: | They propose a ground-truth-free evaluation framework focused on reasoning consistency and instruction following. |
| Outcome: | The proposed framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches. |
SUBS: Subtree Substitution for Compositional Semantic Parsing (2022.naacl-main)
Copied to clipboard
| Challenge: | Semantic parsing models fail at compositional generalization due to lack of reasoning ability. |
| Approach: | They propose to use subtree substitution for compositional data augmentation to increase the number of subtreas with similar semantic functions as exchangeable. |
| Outcome: | The proposed method improves performance on Scan and GeoQuery, and new SOTA on compositional split of GeoQuery. |
CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
Copied to clipboard
Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
| Approach: | They propose a framework for generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. |
| Outcome: | Experiments on four open-domain instruction using the proposed framework validate the effectiveness of CodecLM over the current state-of-the-art. |
DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition (2026.acl-industry)
Copied to clipboard
| Challenge: | Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional IVR systems suffer from error accumulation and high maintenance overhead. |
| Approach: | They propose a large language model-based framework for large-scale POI attribute acquisition at Baidu Maps. |
| Outcome: | The proposed framework outperforms existing IVR systems in 83.9% task success rate while maintaining a low reaction time of 130ms. |