Papers with artificial intelligence
Evalverse: Unified and Accessible Library for Large Language Model Evaluation (2024.emnlp-demo)
Copied to clipboard
| Challenge: | Evalverse is a library that unifies disparate evaluation tools into a single, user-friendly framework. |
| Approach: | They propose to integrate existing evaluation frameworks into a single, user-friendly framework that enables individuals with limited knowledge of artificial intelligence to request LLM evaluations and receive detailed reports. |
| Outcome: | The proposed framework can be used by individuals with limited knowledge of artificial intelligence to request and receive LLM evaluations and receive detailed reports. |
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation (L18-1)
Copied to clipboard
| Challenge: | Currently, there is no widely accepted standard for evaluation of machine translation (MT) for Chinese-to-English translation, there are no standard for standardized training sets, development sets, and test sets. |
| Approach: | They propose to use Chinese-to-English machine translation as a benchmark . they build a highly competitive state-of-the-art MT system that outperforms reported results . |
| Outcome: | The proposed system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years. |
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
Copied to clipboard
| Challenge: | Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments. |
| Approach: | They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics. |
| Outcome: | The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas. |
Creative Natural Language Generation (2023.emnlp-tutorial)
Copied to clipboard
| Challenge: | This tutorial aims to bring awareness of the important and emerging research area of open-domain creative generation. |
| Approach: | They will review recent studies on creative language generation at sentence level as well as longer forms of text. |
| Outcome: | This paper reviews recent studies on creative language generation at sentence level as well as longer forms of text. |
On the Interpretability of Deep Learning Models for Collaborative Argumentation Analysis in Classrooms (2024.acl-srw)
Copied to clipboard
| Challenge: | Existing models for collaborative argumentation lack interpretability and teachers are skeptics about their use. |
| Approach: | They propose to use four explainable AI methods to provide models for automated analysis of argument moves and specificity levels within collaborative argumentation to cultivate trust among teachers. |
| Outcome: | The proposed models perform exceptionally well in analyzing word contributions and demonstrating that the models can be explained by a user-interface. |
Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation (2023.starsem-1)
Copied to clipboard
| Challenge: | Logical formulae are essential for scholars in many fields, including linguistics and artificial intelligence. |
| Approach: | They propose to use a Quantified Boolean Formulae (QBFs) problem to find the shortest formulae as input for a "logic-to-text" generation system. |
| Outcome: | The proposed approach improves the comprehensibility and fluency of the generated texts. |
GOLD: Geometry Problem Solver with Natural Language Description (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for solving geometry math problems struggle with accurately interpreting geometry diagrams, posing a challenge for problem-solving. |
| Approach: | They propose a model that extracts geometric relations from diagrams and converts them into natural language descriptions. |
| Outcome: | The proposed model outperforms the previous best method on the UniGeo dataset by 12.7% and 42.1% in calculation and proving subsets. |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
Copied to clipboard
Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
| Challenge: | Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation. |
| Approach: | They propose a lightweight and extensible framework for Augmented Language Models called Gentopia. |
| Outcome: | The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm. |
Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)
Copied to clipboard
Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
| Challenge: | Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction. |
| Approach: | They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models. |
| Outcome: | The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains. |
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can attain professional-level proficiency in specific domains through fine-tuning. |
| Approach: | They propose a multi-modal LLM that aligns molecular structures with natural language via an instruction-tuning approach. |
| Outcome: | InstructMol surpasses existing models and reduces the gap with specialists in drug discovery tasks. |
Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2025.acl-demo)
Copied to clipboard
| Challenge: | a single AI model is often insufficient for complex tasks, requiring integration of multiple models into pipelines . a conversational agent can build pipelines composed of diverse AI models based on user requirements . |
| Approach: | They propose a conversational agent that constructs AI model pipelines based on user requirements. |
| Outcome: | The proposed agent can build AI model pipelines from human-curated and synthetic data. |
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (N18-1)
Copied to clipboard
| Challenge: | Visual question answering datasets are a form of (visual) Turing test that artificial intelligence should strive to achieve. |
| Approach: | They propose automatic procedures to remedy design deficiencies in visual question answering datasets . they propose to use a set of decoys to re-construct decoying answers for two popular Visual QA datasets. |
| Outcome: | The proposed procedures improve the performance of the proposed datasets. |
Towards Context-Based Violence Detection: A Korean Crime Dialogue Dataset (2024.findings-eacl)
Copied to clipboard
| Challenge: | Currently, there are three main branches of violence detection, including surveillance of potential threats in offline situation and automatic prevention of harmful media. |
| Approach: | They propose to use the Korean Crime Dialogue Dataset to classify violence that occurs in offline scenarios. |
| Outcome: | The proposed dataset shows that understanding varying relationships among interlocutors improves the performance of crime dialogue classification. |
A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |
Learning from Litigation: Graphs for Retrieval and Reasoning in eDiscovery (2025.acl-industry)
Copied to clipboard
| Challenge: | Electronic Discovery (eDiscovery) requires identifying relevant documents from vast collections for legal production requests. |
| Approach: | They propose a system that integrates knowledge graphs for enhanced document ranking and classification, augmented by LLM-driven reasoning. |
| Outcome: | The proposed system outperforms baselines in F1-score, precision, and recall across balanced and imbalanced datasets. |
A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have opened up promising new avenues by enhancing reasoning and inference capabilities across diverse data and information sources. |
| Approach: | They propose a multi-agent framework that facilitates mathematical modeling and data analytics by dynamically generating executable code. |
| Outcome: | The proposed framework outperforms existing models on portfolio management tasks and provides human-readable explanations for its predictions. |
The Margarita Dialogue Corpus: A Data Set for Time-Offset Interactions and Unstructured Dialogue Systems (2020.lrec-1)
Copied to clipboard
| Challenge: | Time-Offset Interaction Applications (TOIAs) simulate face-to-face conversations between humans and digital human avatars recorded in the past. |
| Approach: | They propose a methodology for creating the knowledge base for a TOIA, a dialogue corpus, and baselines for single-turn answer retrieval. |
| Outcome: | The proposed method lets the avatar maker list pairs by intuition, guessing what possible questions a user may ask to the avatar. |
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)
Copied to clipboard
| Challenge: | Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs. |
| Approach: | They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. |
| Outcome: | The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. |
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)
Copied to clipboard
Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu
| Challenge: | Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry. |
| Approach: | They propose to summarize advanced methods through a taxonomy that offers novel perspectives. |
| Outcome: | The proposed method delineates the challenges and future directions, thereby shedding light on future research. |
SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Despite advances in artificial intelligence, building social intelligence remains a challenge. |
| Approach: | They propose a task to explain why people laugh in a video and a dataset to do this. |
| Outcome: | The proposed dataset generates plausible explanations for laughter in video and in-the-wild videos. |
Generalization in Instruction Following Systems (2021.naacl-main)
Copied to clipboard
| Challenge: | Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence. |
| Approach: | They propose a learning strategy that involves data augmentation to improve the model's performance. |
| Outcome: | The proposed learning strategy outperforms state-of-the-art models in the blocks world domain while satisfying our expectations much better. |
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in pretrained language models have shown promising results on commonsense reasoning benchmark datasets. |
| Approach: | They propose a commonsense reasoning benchmark dataset with 4k sentence pairs . they propose 'gamified' model-in-the-loop setup to incentivize challenging samples . |
| Outcome: | The proposed benchmarks show that the proposed model achieves 71% standard accuracy and 51% pairwise accuracy, well below human performance. |
Where to Submit? Helping Researchers to Choose the Right Venue (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Whenever researchers write a paper, the same question occurs: "Where to submit?" In this paper, we introduce "Where To Submit" (WTS) that recommends conferences and journals based on title, abstract, and keywords of a given paper. |
| Approach: | They propose an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. |
| Outcome: | The proposed system achieves an Accuracy@5 of approximately 83% for AI papers and 95% in medicine. |
Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls (2020.coling-main)
Copied to clipboard
| Challenge: | Despite efforts to adopt digital technologies, the success rate in improving business performance is very low due to the lack of a coherent digital strategy. |
| Approach: | They apply NLP models to earnings calls to understand different clusters of digital strategy patterns that companies are Adopting. |
| Outcome: | The proposed models show that Fortune 500 companies use four distinct strategies which are product-led, customer experience-led and service-led. |
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment (2024.emnlp-main)
Copied to clipboard
Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing algorithms for achieving optimal alignment are mostly unidirectional . a recent study suggests that large language models can be ground with evident preferences . |
| Approach: | They propose to ground large language models with evident preferences . they propose to use controllable preference optimization to specify different objectives . |
| Outcome: | The proposed models can provide responses that match various preferences among the ”3H” desiderata. |
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training (C18-1)
Copied to clipboard
| Challenge: | Story generation is a challenging problem in artificial intelligence (AI) . previous work focused on learning statistical models of event sequences from large-scale text corpora . |
| Approach: | They propose to use adversarial training to generate reasonable story endings . their model includes a generator that defines the policy of generating a story ending . |
| Outcome: | The proposed model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6% compared with state-of-the-art baseline methods. |
L4: Mutual Learning Helps Lifelong Language Learning (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing distillation methods rely on domain-specific teachers, limiting their ability to update in real-time and adapt to dynamic environments. |
| Approach: | They propose a framework that enables continuous mutual learning from task streams without relying on domain-specific teachers. |
| Outcome: | The proposed framework reduces catastrophic forgetting while improving performance on various benchmark datasets making it suitable for real-world, dynamic natural language processing (NLP) applications. |
UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | e-commerce tasks such as multimodal retrieval and multimodal generation are largely ignored due to the diversity of the multimodal fashion domain. |
| Approach: | They propose a framework that integrates image generation with retrieval and text generation tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art models across fashion tasks. |
Revealing the Barriers of Language Agents in Planning (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood . |
| Approach: | They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions . |
| Outcome: | The proposed model achieves 15.6% on a real-world planning benchmark. |
Reading Between the Lines: Exploring Infilling in Visual Narratives (2020.emnlp-main)
Copied to clipboard
| Challenge: | Generating long form narratives from multiple modalities requires a model to learn surrounding contextual information by masking spans of input while decoding attempts in generating the entire text. |
| Approach: | They propose to use infilling techniques to generate textual descriptions from images that are rich in contextual dependencies. |
| Outcome: | The proposed model outperforms existing models in visual storytelling by generating text from a large scale dataset of 46,200 procedures and 340k pairwise images and textual descriptions. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
Copied to clipboard
Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Generating Radiology Reports via Memory-driven Transformer (2020.emnlp-main)
Copied to clipboard
| Challenge: | Medical imaging reports are time-consuming and can be error-prone for inexperienced radiologists. |
| Approach: | They propose to generate radiology reports with memory-driven Transformer using relational memory and memory-based conditional layer normalization. |
| Outcome: | The proposed method outperforms existing models on IU X-Ray and MIMIC-CXR . it generates long reports with medical terms and meaningful image-text attention mappings . |
Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
Copied to clipboard
| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
| Outcome: | The proposed model performs well on argument mining and argument generation tasks. |
A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Metaphor identification procedures and selectional preference violations are challenging for machines to recognize and comprehend metaphors. |
| Approach: | They propose a quantum-inspired matching network for metaphor detection based on QLM . metaphors are widely present in the language, thought and behavior of humans . |
| Outcome: | The proposed method can be used to detect metaphors even in the face of conventional metaphors. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
Copied to clipboard
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
Copied to clipboard
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge (2024.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain. |
| Approach: | They propose to decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the task. |
| Outcome: | The proposed method generates more accurate and reliable court views on two real-world datasets LAIC2021 and CJO2022. |
Atoxia: Red-teaming Large Language Models with Target Toxic Answers (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are still vulnerable to generation safety vulnerabilities. |
| Approach: | They propose a method that A**tacks LLMs with target "toxi" given a particular harmful answer, the method generates a user query and a misleading answer opening to examine the internal defects of a given LLM. |
| Outcome: | The proposed method detects safety risks in open-source models and state-of-the-art models such as GPT-4o. |
Understanding “Democratization” in NLP and ML Research (2024.emnlp-main)
Copied to clipboard
| Challenge: | a large number of NLP and ML papers mention terms related to democracy . authors find that democratization is most frequently used to convey (ease of) access to or use of technologies without meaningfully engaging with theories of democratisation. |
| Approach: | They analyze papers using the term "democra*" to clarify how it is understood in NLP and ML . they find that democratization is most frequently used to convey (ease of) access to or use of technologies . |
| Outcome: | The authors analyze papers using the term "democra*" they find that democratization is most frequently used to convey (ease of) access to or use of technologies without meaningfully engaging with theories of democratisation. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
Copied to clipboard
| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
MirasText: An Automatically Generated Text Corpus for Persian (L18-1)
Copied to clipboard
Behnam Sabeti, Hossein Abedi Firouzjaee, Ali Janalizadeh Choobbasti, S.H.E. Mortazavi Najafabadi, Amir Vaheb
| Challenge: | Natural language processing is one of the most important fields of artificial intelligence. |
| Approach: | They propose to use MirasText to generate Persian text corpus from Persian websites . MiraSText has over 2.8 million documents and over 1.4 billion tokens . |
| Outcome: | The generated corpus has over 2.8 million documents and over 1.4 billion tokens . MirasText has over 800 billion token tokens and more than 300 thousand articles . |
Abusive language in Spanish children and young teenager’s conversations: data preparation and short text classification with contextual word embeddings (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on how to automatically detect abusive short texts are gaining interest in the natural language processing community. |
| Approach: | They propose to use a contextual word embedding model to automatically detect abusive short texts for Spanish language. |
| Outcome: | The proposed model outperforms classical methods in the detection of abusive short texts for the spanish language. |
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
| Outcome: | The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor. |
FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP (2023.acl-long)
Copied to clipboard
| Challenge: | federated learning (FL) is a promising technique for preserving data privacy . however, there is no work on applying FL to legal NLP . |
| Approach: | They propose to use federated learning to train models in a collaborative way without sharing data . they propose to test the FL benchmark on real-world legal data from Chinese courts . |
| Outcome: | The proposed benchmark combines five legal NLP tasks and one privacy task on Chinese courts. |
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions (2022.findings-acl)
Copied to clipboard
Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, Deniz Yuret
| Challenge: | Existing models with similar physical and causal understanding capabilities are still underdeveloped. |
| Approach: | They propose a video question answering dataset that requires causal reasoning about physical forces and object interactions. |
| Outcome: | The proposed dataset requires causal reasoning about physical forces and object interactions. |
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors (P18-1)
Copied to clipboard
Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, AmirAli Bagher Zadeh, Louis-Philippe Morency
| Challenge: | Multimodal research is a growing field of artificial intelligence, and fusion is one of the main research problems. |
| Approach: | They propose a low-rank multimodal fusion method which integrates multiple unimodal representations into one compact multimodal representation. |
| Outcome: | The proposed method achieves competitive results on multimodal sentiment analysis, speaker trait analysis, and emotion recognition tasks while reducing computational complexity. |
Talk2Car: Taking Control of Your Self-Driving Car (D19-1)
Copied to clipboard
| Challenge: | a long-term goal of artificial intelligence is to have an agent execute commands through natural language. |
| Approach: | They propose to use a dataset to compare commands written in natural language for self-driving cars with other datasets. |
| Outcome: | The proposed task is a challenging one and shows promising results, the authors argue . the talk2car dataset compares with similar datasets and shows that the proposed task requires additional research in natural language processing and computer vision. |
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples . |
| Approach: | They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE. |
| Outcome: | The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR . |
CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in artificial intelligence highlight the potential of language models in psychological health support. |
| Approach: | They propose a method to enhance the precision and efficacy of psychological support through large language models. |
| Outcome: | The proposed model generates professional and structured responses in Chinese psychological health Q&A tasks, showcasing its practicality and quality. |
Comparing the Evaluation and Production of Loophole Behavior in Humans and Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that loophole-seeking is frequent and intuitive in children . a large number of models capture the pragmatic understanding required for loopholes, says a researcher . |
| Approach: | a study compares large language models to humans to examine loophole behavior . they found that models struggle to recognize humor in creative exploitation of loopholes . |
| Outcome: | a study compares state-of-the-art models to humans to examine loophole behavior in humans . a large language model can generate loopholes, but only two are capable of generating them . |
LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers (2023.emnlp-main)
Copied to clipboard
Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, Roger Levy
| Challenge: | Logical reasoning is an important task for artificial intelligence, says a new study . many prompting-based strategies to enable large language models fail in subtle and unpredictable ways. |
| Approach: | They propose to reformulate logical reasoning tasks by leveraging large language models . they use a modular neurosymbolic programming approach to translate premises and conclusions from natural language to logic . |
| Outcome: | The proposed approach outperforms open-source models on FOLIO and ProofWriter while showing distinct failure modes. |
In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in artificial intelligence have led to the creation of highly capable large language models (LLMs) that can perform tasks in a human-like manner, but lack infant-level cognitive abilities in certain areas. |
| Approach: | They designed a text-based multi-choice QA scenario similar to the A-Not-B error to test their inhibitory control abilities. |
| Outcome: | The proposed model shows that state-of-the-art LLMs perform well with in-context learning but make errors and show a drop of as many as 83.3% in reasoning tasks when the context changes trivially. |
Analyzing Key Factors Influencing Emotion Prediction Performance of VLLMs in Conversational Contexts (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that large language models and vision large language model (VLLMs) possess EI and the ability to understand emotional stimuli in the form of text and images. |
| Approach: | They analyze the key elements affecting the emotion prediction performance of VLLMs in conversational contexts. |
| Outcome: | The proposed model performance was compared with other models in a conversational context. |
Generating Accurate Electronic Health Assessment from Medical Graph (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models based on medical domain-specific knowledge or patients’ prior diagnoses and clinical encounters were mainly based upon clinical diagnoses. |
| Approach: | They propose a graph neural network model that incorporates clinical knowledge into an end-to-end corpus-learning system and builds on it. |
| Outcome: | The proposed model significantly improves the BLEU and rouge score compared with baseline models and physicians’ evaluation showed that it generates high-quality assessments. |
Responsible Evaluation of AI for Mental Health (2026.acl-long)
Copied to clipboard
Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych
| Challenge: | Existing approaches to evaluating AI tools in this domain remain fragmented and inconsistent. |
| Approach: | They propose a taxonomy of AI mental health support types that integrates clinical soundness, social context, and equity to provide a structured basis for evaluation. |
| Outcome: | The proposed framework integrates clinical soundness, social context, and equity, providing a structured basis for evaluation. |
SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory. |
| Approach: | They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. |
| Outcome: | The proposed framework can achieve highly competitive relational expressiveness without increasing model complexity. |
MADD: Multi-Agent Drug Discovery Orchestra (2025.findings-emnlp)
Copied to clipboard
Gleb Vitalevich Solovev, Alina Borisovna Zhidkovskaya, Anastasia Orlova, Nina Gubina, Anastasia Vepreva, Rodion Golovinskii, Ilya Tonkii, Ivan Dubrovsky, Ivan Gurev, Dmitry Gilemkhanov, Denis Chistiakov, Timur A. Aliev, Ivan Poddiakov, Galina Zubkova, Ekaterina V. Skorb, Vladimir Vinogradov, Alexander Boukhanovsky, Nikolay Nikitin, Andrei Dmitrenko, Anna Kalyuzhnaya, Andrey Savchenko
| Challenge: | Recent advances in artificial intelligence have limited access to wet-lab tools for hit identification . multi-agent systems combine interpretability of LLMs with precision of specialized models and tools . |
| Approach: | They propose a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. |
| Outcome: | The proposed system reduces the complexity of traditional screening methods and improves efficiency. |
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages. |
| Approach: | They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
| Outcome: | The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour (2022.lrec-1)
Copied to clipboard
| Challenge: | Forced labour is the most common type of modern slavery, affecting at least 24.9 million people worldwide. |
| Approach: | They propose to annotate an English corpus for multi-class and multi-label forced labour detection using specialised data from specialised sources. |
| Outcome: | The proposed corpus consists of 989 news articles annotated according to risk indicators defined by the International Labour Organization (ILO). |
World Class Language Technology - Developing a Language Technology Strategy for Danish (2020.lrec-1)
Copied to clipboard
| Challenge: | Danish government adopts ambitious strategy for LT and artificial intelligence . 35 million DKK will be spent over a period of 6 years to develop platform . |
| Approach: | They describe the process behind the development of the language-related parts of the strategy . they describe how focus areas and recommendations for the LT strategy were established . |
| Outcome: | The Danish government adopted a new, ambitious strategy for LT and AI in March 2019 . the focus areas and recommendations for the LT strategy were established based on user feedback . |
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, including mathematical problem-solving. |
| Approach: | They propose a framework that connects the subgoal breakdown process and the probability of solving problems by identifying better subgoals with theoretical guarantees. |
| Outcome: | The proposed framework outperforms existing methods on two benchmarks, GSM8K and MATH, highlighting the potential of SEGO in AI-driven mathematical problem-solving. |
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples (2022.lrec-1)
Copied to clipboard
| Challenge: | a commonsense knowledge resource organizes common sense that is not necessarily correct all the time, but most people are expected to know or believe. |
| Approach: | They propose a machine learning-based approach to detect semantic gaps in a commonsense knowledge graph . they use a conceptNet dataset to test the validity of two adjacent triples . |
| Outcome: | The proposed approach detects a semantic gap in a commonsense knowledge graph . the proposed approach also provides insights into the effectiveness of sense embeddings . |
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks or datasets require only a few steps of reasoning, making it difficult to analyse AI’s behaviour with reference to different problems within a specific topic in detail. |
| Approach: | They propose a conic10K math problem dataset that requires only a few steps of reasoning to be analysed. |
| Outcome: | The proposed dataset shows that existing language models exhibit weak performance on complex reasoning. |
Interpretable Semantic Gradients in SSD: A PCA Sweep Approach and a Case Study on AI Discourse (2026.findings-acl)
Copied to clipboard
| Challenge: | Supervised Semantic Differential (SSD) is a mixed quantitative–interpretive method that models how text meaning varies with continuous individual-difference variables . currently no systematic method exists for choosing the number of retained components, introducing avoidable researcher degrees of freedom in the analysis pipeline. |
| Approach: | They propose a PCA sweep procedure that treats dimensionality selection as a joint criterion over representation capacity, gradient interpretability, and stability across nearby values of K. |
| Outcome: | The proposed method is based on a corpus of short posts about artificial intelligence written by Prolific participants who also completed Admiration and Rivalry narcissism scales. |
Compositional Generalization in Dependency Parsing (2022.acl-long)
Copied to clipboard
| Challenge: | Compositionality is the ability to combine familiar units like words into novel phrases and sentences. |
| Approach: | They introduce a set of dependency parses for Compositional Freebase Queries (CFQ) they analyze the behaviour of a state-of-the-art dependency parser on the CFQ dataset . |
| Outcome: | The proposed dependency parser performs lower on the most challenging splits with the highest compound divergence. |
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. |
| Approach: | They propose a federated framework for the Chain-of-Thought distillation of knowledge from LLMs to SLMs, while adhering to privacy requirements. |
| Outcome: | The proposed framework ensures secure knowledge transfer from an LLM on a high-powered server to an SLM on resource-constrained client while adhering to privacy requirements. |
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in knowledge base construction techniques focus on the acquisition of positive (true) KB statements, but negative (false) statements are important for discriminative reasoning. |
| Approach: | They propose a framework that ranks potential negatives in commonsense KBs using a contextual language model. |
| Outcome: | The proposed framework ranks negatives in commonsense KBs using a language model . it yields positives that are more grammatical, coherent, and informative . |
Cross-modal Memory Networks for Radiology Report Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Medical imaging reports are essential in clinical practice, and generating the reports is beneficial to reduce the burden of radiologists. |
| Approach: | They propose to use a shared memory to enhance the encoder-decoder framework for radiology report generation. |
| Outcome: | The proposed model can generate more accurate reports on two widely used datasets. |
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning. |
| Approach: | They propose a framework that enables LLMs to create their own tools using documentation and code realization. |
| Outcome: | The proposed framework outperforms existing chain-of-thought, program-of thought, and tool-using baselines on MATH and TabMWP benchmarks. |
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing foundation models are limited in access to diverse modalities and privacy regulations restrict the development of comprehensive foundation models. |
| Approach: | They propose a knowledge injection approach to extract and inject healthcare knowledge into medical foundation models to enhance their ability to handle multiple tasks and modalities. |
| Outcome: | The proposed method preserves privacy and enhances the model’s ability to handle complex medical tasks involving multiple modalities. |
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020.acl-main)
Copied to clipboard
| Challenge: | Legal Artificial Intelligence (LegalAI) focuses on applying artificial intelligence to help legal tasks. |
| Approach: | They introduce the history, current state, and future directions of research in LegalAI . they illustrate the tasks from the perspectives of legal professionals and NLP researchers . |
| Outcome: | The proposed system can reduce heavy and redundant work for legal professionals . it can also provide a reliable reference to those who are not familiar with the legal domain . |
Investigating and Extending Homans’ Social Exchange Theory with Large Language Model based Agents (2025.acl-long)
Copied to clipboard
| Challenge: | Social exchange theory (SET) is widely recognized as a basic framework for understanding human interactions and interactions. |
| Approach: | They propose to use large language models to study Homans’ social exchange theory (SET) by constructing a virtual society composed of three LLM agents and having them engage in a social exchange game to observe their behaviors. |
| Outcome: | The proposed model extends Homans’ SET with LLM-based agents and demonstrates consistency between the agent and human behavior. |
Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation approaches for large language models (LLMs) rely on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. |
| Approach: | They propose to evaluate LLMs by designing new tasks, automatically generating evaluation datasets for the tasks, and conducting detailed error analyses to scrutinize LLM's adaptability to new tasks. |
| Outcome: | The proposed method examines LLMs’ adaptability to new tasks, their sensitivity to prompt variations, and their error tendencies. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
Copied to clipboard
Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024.acl-long)
Copied to clipboard
| Challenge: | Generating long-term texts using artificial intelligence has always been a challenge . however, the generated novels exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. |
| Approach: | They propose a method for extracting excelsior and expanding from novel data to generate arbitrarily long novels using large language models. |
| Outcome: | The proposed method produces high-quality long-form novels with a high level of logical coherence and appeal despite the use of large language models. |
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Quantization-aware training (QAT) is a low-bit training solution that requires substantial training resources. |
| Approach: | They propose an algorithm that reduces memory consumption by low-bit representations with minimal accuracy loss. |
| Outcome: | EfficientQAT achieves 2-bit Llama-2-70B model on single GPU in 41 hours . compared to previous methods, it obtains model with less than 3 points accuracy degradation . |
Bridging Fairness and Environmental Sustainability in Natural Language Processing (2022.emnlp-main)
Copied to clipboard
| Challenge: | a lack of research on the interplay between fairness and environmental impact is a problem in natural language processing . fairness is prone to encode and amplify stereotypical social biases, according to several studies . |
| Approach: | They evaluate a technique to reduce energy consumption of English NLP models by knowledge distillation for its impact on fairness. |
| Outcome: | The proposed method reduces energy consumption and environmental impact of English NLP models. |
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models with instruction-following capabilities have revolutionized the field of artificial intelligence. |
| Approach: | They propose an annotation-free framework for empowering large language models with instruction-following capabilities. |
| Outcome: | The proposed framework generates multi-turn multimodal instruction-response conversations from a language model. |
Regulation and NLP (RegNLP): Taming Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | polarization in AI safety and ethics debates are swaying political agendas on AI regulation and governance . regulation studies are rich source of knowledge on how to systematically deal with risk and uncertainty . |
| Approach: | They argue that NLP research can benefit from proximity to regulatory studies . they argue that regulation studies should focus on linking scientific knowledge to regulatory processes . |
| Outcome: | The proposed research space should focus on linking scientific knowledge to regulatory processes based on systematic methodologies. |
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for generating textual-based explanations are highly implausible and damage a user’s trust in the automated system. |
| Approach: | They propose a method which first applies robust transformer models on a real-world, up-to-date, self-collected mergers and acquisitions dataset and then generates plausible, post-hoc, counterfactual explanations. |
| Outcome: | The proposed model improves model accuracy and human performance while generating plausible explanations based on human trials. |
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic (2023.acl-long)
Copied to clipboard
| Challenge: | Despite the recent advances in distributed representation and neural networks, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. |
| Approach: | They propose to use logic formalism to perform systematic attacks centring around natural logic to generate better adversarial examples with fewer visits to the victim models. |
| Outcome: | The proposed framework generates better adversarial examples with fewer visits to the victim models. |
AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic (2025.coling-main)
Copied to clipboard
| Challenge: | Existing benchmarks for large language models (LLMs) in Arabic are lacking . despite progress in their development, there is a lack of comprehensive trustworthiness evaluation benchmarks . |
| Approach: | They propose to use Arabic as a language to assess trustworthiness of large language models. |
| Outcome: | The proposed benchmark measures the trustworthiness of large language models in Arabic. |
Annotated Corpus of Scientific Conference’s Homepages for Information Extraction (L18-1)
Copied to clipboard
| Challenge: | a corpus of scientific conferences contains homepages with annotations of important information . name of conference, abbreviation, place, submission, notification, camera ready dates are included . |
| Approach: | They propose a corpus that contains 943 homepages of scientific conferences with annotations of interesting information. |
| Outcome: | The proposed corpus contains 943 homepages of scientific conferences, 14794 including subpages . the results show that it can be used as a reference data set for this type of task. |
EpiK-Eval: Evaluation for Language Models as Epistemic Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Developing systems that can reason through language understanding has been a cornerstone in natural language processing research. |
| Approach: | They propose a question-answering benchmark to evaluate LLMs' ability to combine knowledge from different training documents within their parameter space. |
| Outcome: | The proposed benchmark aims to evaluate LLMs' ability to combine knowledge from different training documents within their parameter space. |
Revisiting the Evaluation of Theory of Mind through Question Answering (D19-1)
Copied to clipboard
| Challenge: | Existing benchmarks for theory of mind are flawed due to dataset biases . evaluators have been using the Sally-Anne test to infer false beliefs in others . |
| Approach: | They propose to use question answering to evaluate theory of mind . they propose to explicitly control for data regularities via a careful examination of the answer space . |
| Outcome: | The proposed evaluation protocol and dataset control for data regularities via a careful examination of the answer space. |
LIDDIA: Language-based Intelligent Drug Discovery Agent (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks. |
| Approach: | They propose an autonomous agent capable of intelligently navigating the drug discovery process in silico. |
| Outcome: | The proposed agent can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets and intelligently balances exploration and exploitation in the chemical space. |
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning (2024.acl-long)
Copied to clipboard
Shivalika Singh, Freddie Vargus, Daniel D’souza, Börje Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura O’Mahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergun, Ifeoma Okoh, Aisha Alaagib, Oshan Mudannayake, Zaid Alyafeai, Vu Chien, Sebastian Ruder, Surya Guthikonda, Emad Alghamdi, Sebastian Gehrmann, Niklas Muennighoff, Max Bartolo, Julia Kreutzer, Ahmet Üstün, Marzieh Fadaee, Sara Hooker
| Challenge: | Existing datasets in the English language are mostly in the realm of instruction fine-tuning . aya dataset, the Aya Collection, and the AYa Evaluation Suite are key resources . |
| Approach: | They aim to build a human-curated instruction-following dataset spanning 65 languages . they work with fluent speakers of languages from around the world to collect natural instances of instructions and completions . |
| Outcome: | The goal is to build a human-curated instruction-following dataset spanning 65 languages. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
Copied to clipboard
Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Is GPT-4 a Good Data Analyst? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc. |
| Approach: | They propose to use GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. |
| Outcome: | The proposed framework compares GPT-4 with human data analysts to perform end-to-end data analysis with databases from a wide range of domains. |
Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve the mathematical reasoning capabilities of Large Language Models (LLMs) are limited due to the proprietary nature of the data. |
| Approach: | They propose a data synthesis method that generates large-scale mathematical reasoning datasets using lightweight 7B-scale models. |
| Outcome: | The proposed method outperforms existing open-source datasets in both in-domain and out-of-domain evaluations and shows improvements in code reasoning tasks. |
Exploring Semantic Capacity of Terms (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that measure semantic capacity of terms are not all considered equal . a good command of semantic capacity will give us more insight into the granularity of terms . |
| Approach: | They propose a model that evaluates semantic capacity of terms if text corpus can provide enough co-occurrence information of terms. |
| Outcome: | The proposed model can evaluate semantic capacity of terms if the corpus can provide enough co-occurrence information of terms. |
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset (2022.lrec-1)
Copied to clipboard
Tiezheng Yu, Rita Frieske, Peng Xu, Samuel Cahyawijaya, Cheuk Tung Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages. |
| Approach: | They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model . |
| Outcome: | The proposed model improves on the biggest existing dataset, Common Voice zh-HK. |
NYAYAANUMANA and INLEGALLLAMA: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis (2025.coling-main)
Copied to clipboard
Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Shivam Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
| Challenge: | In India, a significant backlog of cases burdens the legal system. |
| Approach: | They present a corpus of 7,02,945 preprocessed Indian legal cases compiled for LJP . they use a domain-specific generative large language model tailored to the intricacies of the legal system . |
| Outcome: | The proposed dataset surpasses existing datasets like PredEx and ILDC, and improves prediction accuracy and comprehensible explanations. |
The discordance between embedded ethics and cultural inference in large language models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Effective interactions between AI and humans require an accurate representation of diverse cultures. |
| Approach: | They propose a framework that embeds ethical principles within an LLM and a hyperplane that embedding cultural norms within it. |
| Outcome: | The proposed framework shows that cultural norms are more aligned with ethical principles than standard models. |
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on multi-modal dialogue pre-training is limited due to limited availability of multi-dimensional data . a recent emergence of chatGPT 1 has increased confidence in the potential for this goal . |
| Approach: | They propose a framework for multi-modal dialogue pre-training that integrates experts to accommodate multi-faceted tasks. |
| Outcome: | The proposed framework achieves state-of-the-art on eight multi-modal dialog benchmarks. |
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Objective questions such as fill-in-the-blank and multiple-choice require examinees to select one valid answer from a set of invalid options. |
| Approach: | They examine distractor generation tasks, datasets, methods, and evaluation metrics for English objective questions. |
| Outcome: | The proposed task is based on fill-in-the-blank and multiple choice questions and is widely utilized in educational settings across various domains and subjects. |
Gender Representation in Open Source Speech Resources (2020.lrec-1)
Copied to clipboard
| Challenge: | Using open source corpora, we find that gender balance depends on other corpus characteristics such as elicited/non ellicite vs. non-eliciting speech, low/high resource language, speech task targeted. |
| Approach: | They propose to use open source corpora to find gender information in spoken language systems . they propose metadata and recommendations for researchers to assure better transparency . |
| Outcome: | The proposed method improves the quality and transparency of open source speech resources. |
A Survey of Deep Learning for Mathematical Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | a survey of deep learning for mathematical reasoning examines the field . a comprehensive reading list is provided to assist readers interested in the field. |
| Approach: | They present a survey of deep learning for mathematical reasoning over the past decade . they outline directions for future research and highlight potential for further exploration . |
| Outcome: | The proposed framework is based on the results of a decade-long survey of deep learning for mathematical reasoning. |
Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks (2026.acl-long)
Copied to clipboard
| Challenge: | Abstraction and Reasoning Corpus and ARC-AGI are widely used to assess progress in artificial intelligence. |
| Approach: | They propose a two-stage pipeline that separates perception and reasoning . they propose to test this pipeline against standard end-to-end one-stage evaluation . |
| Outcome: | The proposed pipeline separates perception and reasoning, and isolates reasoning from bottlenecks. |
LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)
Copied to clipboard
Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria
| Challenge: | Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs). |
| Approach: | They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions . |
| Outcome: | The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals. |
Gazelle: An Instruction Dataset for Arabic Writing Assistance (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in generative AI have transformed the landscape of writing assistance, especially through the development of Large Language Models (LLMs). |
| Approach: | They propose to use a dataset to evaluate leading LLMs to improve their writing assistance tools in Arabic. |
| Outcome: | The proposed dataset highlights the strengths and limitations of leading LLMs, including GPT-**4**, GPT**4o**, Cohere Command R+, and Gemini **1.5** Pro. |
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings (2025.acl-long)
Copied to clipboard
| Challenge: | Visual persuasion uses visual elements to influence cognition and behaviors . lack of comprehensive data sets connect persuasiveness of images with personal information . |
| Approach: | They propose to use a dataset to connect persuasiveness with personal information . they find psychological characteristics enhance the generation and evaluation of persuasive images . |
| Outcome: | The proposed dataset provides persuasiveness scores of images evaluated by human annotators along with demographic and psychological characteristics. |
Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multimodal UNcommonsense (MUN) is a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations. |
| Approach: | They propose a retrieval-based in-context learning framework that transfers reasoning capabilities from larger models to smaller ones without additional training. |
| Outcome: | The proposed method improves on baseline ICL methods by 8.3% over previous methods. |
Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) face challenges in maintaining accuracy due to the dynamic nature of world knowledge. |
| Approach: | They propose to use a benchmark dataset to investigate the effects of model edits on model safety metrics and guardrails. |
| Outcome: | The proposed dataset sheds light on how the edits, impact the model’s safety metrics and guardrails. |
RESEMO: A Benchmark Chinese Dataset for Studying Responsive Emotion from Social Media Content (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on social media text processing do not focus on responsive emotion analysis. |
| Approach: | They propose a Chinese dataset named ResEmo for responsive emotion analysis, including 3813 posts with 68,781 comments collected from Weibo, the largest social media platform in China. |
| Outcome: | The proposed dataset includes 3813 posts with 68,781 comments collected from weibo, the largest social media platform in China. |
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024.lrec-main)
Copied to clipboard
| Challenge: | Multimodal semantic understanding is crucial for developing machines capable of interpreting complex interplay of text and visual information. |
| Approach: | They propose a multi-modal soft prompt framework that integrates three experts of soft prompts . they propose sarcasm detection and sentiment analysis tasks that are critical for few-shot learning . |
| Outcome: | The proposed model outperforms the 8.2B model InstructBLIP with 2% parameters . it significantly outperformed other prompt methods on VLMs or task-specific methods . |
Improved Unbiased Watermark for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Unbiased watermarks allow to distinguish between text generated by humans and machines without causing distortion. |
| Approach: | They introduce a family of unbiased, Multi-Channel-based watermarks that partition the language model into segments and promote token probabilities within a selected segment based on a watermark key. |
| Outcome: | The proposed watermarks preserve the original distribution of the language model and offer significant improvements in detectability and robustness over existing unbiased watermark systems. |
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection. |
| Approach: | They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states. |
| Outcome: | The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure. |
VER: Unifying Verbalizing Entities and Relations (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a new model for verbalizing entities and relations is proposed to help understand entities and relationships . a unified model for Verbalizing Entities and Relations is proposed . |
| Approach: | They propose a model that takes any entity or entity set as input and generates a sentence to represent entities and relations. |
| Outcome: | The proposed model can generate sentences describing entities and relations . it can be used to explain entities and relationships, and to perform commonsense reasoning tasks . |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
Copied to clipboard
Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
Math Neurosurgery: Isolating Language Models’ Math Reasoning Abilities Using Only Forward Passes (2025.acl-long)
Copied to clipboard
| Challenge: | Math reasoning is an active area of Large Language Model (LLM) research because it is a hallmark of artificial intelligence and has implications in several domains, including math education. |
| Approach: | They propose a method to isolate math-specific parameters in LLMs using only forward passes. |
| Outcome: | The proposed method improves a model's performance on GSM8K and MATH by 4-17% while leaving non-math behavior unaltered. |
Related Work Is All You Need (2024.lrec-main)
Copied to clipboard
| Challenge: | a corpus of 400k annotations of related work is used to generate a "related work" section . authors and researchers often turn to tools like Google Scholar to find related research for their papers . |
| Approach: | They propose to use a corpus with 400k annotations to generate a "related work" section . they propose to automate the process by using a newly-released corpus that contains human annotations . |
| Outcome: | The proposed technique can be automated by using human annotations of related work sections. |
A rebuttal of two common deflationary stances against LLM cognition (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are arguably the most predictive models of human cognition available. |
| Approach: | They argue that these deflationary claims need further justification . they argue that large language models are "just" simplistic entities . |
| Outcome: | The proposed models lack critical capacities, but they are not "just" models, the authors argue . they argue that the arguments need to be weighed against the evidence . |
VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing Large Multi-modal Models lack a robust visual processing capability that is often masked by evaluation metrics that prioritize final-answer accuracy. |
| Approach: | They propose a three-layer evaluation framework that scrutinizes the generation of valid visual aids and the soundness of subsequent reasoning steps. |
| Outcome: | The proposed framework examines the generation of valid visual aids and the soundness of subsequent reasoning steps on state-of-the-art models. |
Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series? (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) and Multimodal LLMs (MLLMs) show strong performance in complex reasoning tasks, but their ability to extract symbolic laws from time series data remains underexplored. |
| Approach: | They propose a benchmark to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery. |
| Outcome: | The proposed framework integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system. |
GUIDE: Towards Scalable Advising for Research Ideas (2026.acl-long)
Copied to clipboard
| Challenge: | Existing systems that provide detailed, constructive feedback on academic papers struggle with review fidelity. |
| Approach: | They explore factors that underlie the development of robust advising systems . large language models have shown remarkable progress in tasks from text generation to code synthesis . |
| Outcome: | The proposed model outperforms general-purpose language models in acceptance rates for self-ranked top-30% submissions to ICLR 2025. |
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026.findings-acl)
Copied to clipboard
Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma
| Challenge: | Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks . |
| Approach: | They propose a framework for LLM agent memory mechanisms that formalizes the development process into three stages: storage, reflection, and experience. |
| Outcome: | The proposed framework breaks the development process into three stages . it analyzes the need for long-range consistency, challenges in dynamic environments, and the ultimate goal of continual learning. |