Papers with SoTA
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering (2021.naacl-demos)
Copied to clipboard
| Challenge: | Using a node-based framework, knowledge base question answering systems can grasp structural mappings between questions and KB queries. |
| Approach: | They propose a node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. |
| Outcome: | The proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. |
A Grounded Preference Model for LLM Alignment (2024.findings-acl)
Copied to clipboard
Tahira Naseem, Guangxuan Xu, Sarathkrishna Swaminathan, Asaf Yehudai, Subhajit Chaudhury, Radu Florian, Ramón Astudillo, Asim Munawar
| Challenge: | Large Language Models (LLMs) suffer from factual inconsistency and hallucination despite recent advances . training a preference model requires substantial human annotation, which is expensive and labor-intensive. |
| Approach: | They propose to generate synthetic grounded preference data and train a Grounded Preference Model to assess the overall quality of grounded responses. |
| Outcome: | The proposed model can generate much better grounded responses as judged by GPT4 and achieves the TRUE faithfulness Benchmark. |
Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for hyperbole and metaphor detection focus on superficial text features, ignoring the associations of hyperbola and metaphor . Existing frameworks focus on identifying superficial text, focusing on superficial features . |
| Approach: | They propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction. |
| Outcome: | The proposed framework outperforms baseline methods on four datasets. |
CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing code LLMs adopt a specific architecture or rely on a unified encoder-decoder network for downstream tasks, lacking flexibility to operate in the optimal architecture for a particular task. |
| Approach: | They propose to initialize code LLMs with frozen off-the-shelf LLM and explore instruction-tuning to align with natural language instructions. |
| Outcome: | The proposed model outperforms open-source LLMs on 20 code-related benchmarks. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
Copied to clipboard
| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
Language Models are Crossword Solvers (2025.naacl-long)
Copied to clipboard
| Challenge: | Modern crossword models demonstrate astounding skills in reasoning, coding, wordplay, question answering, and a multitude of other tasks. |
| Approach: | They propose a search algorithm that generalizes well and can support answers with sound rationale by solving full crossword grids with out-of-the-box LLMs. |
| Outcome: | The proposed model outperforms state-of-the-art models in solving crossword grids for the first time and generalizes well. |
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP (2021.acl-long)
Copied to clipboard
| Challenge: | Transfer learning has yielded state-of-the-art (SoTA) results, but annotated data for every target task in every target language is rare, especially for low-resource languages. |
| Approach: | They propose a framework for unsupervised data augmentation for zero-resource transfer learning scenarios that performs simultaneous self-training with data hausse and unsupervised sample selection. |
| Outcome: | The proposed framework outperforms baselines on three zero-resource transfer tasks and achieves SoTA results in all the tasks. |
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues (2021.acl-long)
Copied to clipboard
| Challenge: | Using a large-scale dataset, we explore Chinese named entity recognition (NER) with both textual and acoustic contents. |
| Approach: | They propose a Chinese multimodal named entity recognition dataset . their corpus contains 42,987 annotated sentences and 71 hours of speech data . |
| Outcome: | The proposed model yields state-of-the-art (SoTA) results on Chinese multimodal named entity recognition (NER) based on 42,987 annotated sentences and 71 hours of speech data. |
On the Role of Key Phrases in Argument Mining (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to argument mining often overlook crucial conceptual links between ACs and ARs. |
| Approach: | They propose a framework that extracts key phrases from AM benchmarks using an open-source Large Language Model. |
| Outcome: | The proposed framework surpasses baselines on three structurally distinct AM benchmarks by up to 9.5% F1 score, demonstrating its strong potential. |
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design (2025.naacl-long)
Copied to clipboard
| Challenge: | Using Mixture-of-Experts, researchers have found that efficient MoE is difficult to achieve due to two key reasons: imbalanced expert activation and massive communication overhead. |
| Approach: | They propose a collaboration-constrained routing strategy that encourages more specialized expert groups and leverages expert specialization. |
| Outcome: | The proposed approach achieves an average performance improvement of 0.51% and 0.33% on LLaMA-MoE and Qwen-MaE respectively. |
Advancing Multi-Criteria Chinese Word Segmentation Through Criterion Classification and Denoising (2023.acl-long)
Copied to clipboard
| Challenge: | Recent research on multi-criteria Chinese word segmentation focuses on building complex private structures, adding more handcrafted features, or introducing complex optimization processes. |
| Approach: | They propose a model that fits multiple Chinese word segments using input-hint inputs. |
| Outcome: | The proposed model achieves state-of-the-art (SoTA) performance on multiple datasets simultaneously. |
ADELIE: Aligning Large Language Models on Information Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) struggle to follow complex instructions of IE tasks due to not being aligned with humans. |
| Approach: | They propose an aligned large language moDEL that effectively solves various IE tasks including closed IE, open IE and on-demand IE. |
| Outcome: | The proposed model achieves state-of-the-art (SoTA) performance among open-source models. |
A Conditional Splitting Framework for Efficient Constituency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | Developing efficient and effective parsing solutions has always been a key focus in NLP. |
| Approach: | They propose a generic seq2seq parsing framework that casts constituency parsers into a series of conditional splitting decisions. |
| Outcome: | The proposed framework outperforms state-of-the-art (SoTA) methods in discourse parsing . it is based on a syntactic and discourse parsed model and is linear in number of nodes . |
PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to evaluate privacy leakage in LLMs use memorized prefixes or simple instructions to extract data, which well-aligned models can easily block. |
| Approach: | They propose a framework targeting Personally Identifiable Information (PII) that uses in-context learning to build a privacy context and iteratively updates it with three gradient-based strategies to elicit target PII. |
| Outcome: | The proposed framework outperforms baseline methods and achieves state-of-the-art (SoTA) results on four white-box and two black-box LLMs. |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
Copied to clipboard
Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
| Approach: | They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently . |
| Outcome: | The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width. |
Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding (2022.emnlp-main)
Copied to clipboard
| Challenge: | Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in an image using only caption-image annotations. |
| Approach: | They propose a novel contrastive learning framework that adaptively refines the target prediction by using only caption-image annotations. |
| Outcome: | The proposed framework outperforms existing methods on two widely used benchmarks, Flickr30K Entities and RefCOCO+. |
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)
Copied to clipboard
Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining. |
| Approach: | They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead. |
| Outcome: | The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes. |
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2020.acl-main)
Copied to clipboard
| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
| Outcome: | The proposed method achieves 3.7% to 6.4% Pearson’s r over single-source word embeddings on the STS Benchmark and on the StS12-STS16 datasets. |
HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to answer multi-hop questions are query-agnostic and the extracted facts are ambiguous as they lack context. |
| Approach: | They propose to use a knowledge graph to extract query-relevant information from unstructured text. |
| Outcome: | The proposed method achieves performance improvements on two popular datasets. |
KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)
Copied to clipboard
Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency. |
| Approach: | They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task. |
| Outcome: | The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability. |
Set Generation Networks for End-to-End Knowledge Base Population (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge base population systems require a machine translation task to generate multiple facts, but the fact order is not considered. |
| Approach: | They propose a knowledge base population task that aims to discover facts about entities from texts and expand a KB with these facts. |
| Outcome: | The proposed networks achieve state-of-the-art (SoTA) performance on two benchmark datasets. |
Exploring Large Language Models for Classical Philology (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. |
| Approach: | They propose to use encoder-only and encoder decoder architectures to create four models for Ancient Greek that vary along two dimensions for tasks of interest for Classical languages. |
| Outcome: | The proposed models improve on existing models of Ancient Greek and Latin and provide a large pre-training corpus for Ancient Greek to support the creation of a larger, comparable model zoo for Classical Philology. |
Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context. |
| Approach: | They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework. |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine. |
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |