Papers with language models
LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts (2026.eacl-long)
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| Challenge: | LM-LEXICON is a definition modeling approach that integrates data clustering, semantic expert learning, and model merging. |
| Approach: | They propose a definition modeling approach that integrates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. |
| Outcome: | The proposed model outperforms existing methods on five widely used benchmarks and achieves a BLEU score of 7%. |
Sampling Informative Training Data for RNN Language Models (P18-3)
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| Challenge: | Existing methods for selecting training data for recurrent neural network language models are limited by computational costs. |
| Approach: | They propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNN) language models. |
| Outcome: | The proposed approach outperforms models trained on the Billion Word and Wikitext-103 benchmark corpora. |
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models (2022.starsem-1)
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| Challenge: | ArgumentAnalyst is a multi-dimensional, modular framework for performing deep argument analysis using existing pre-trained language models (PTLMs). |
| Approach: | They propose a multi-dimensional, modular framework for performing deep argument analysis using current pre-trained language models (PTLMs) ArgumentAnalyst reconstructs argumentative texts as valid arguments by inserting missing premises and conclusions, formalizing inferences, and coherently linking the reconstruction to the source text. |
| Outcome: | The proposed framework can replicate established heuristics, cope with plurality of correct solutions, and exploit higher-order evidence. |
DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)
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Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi
| Challenge: | Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. |
| Approach: | They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks. |
| Outcome: | The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach. |
What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models (2020.tacl-1)
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| Challenge: | Pretraining by language modeling has become popular but we have yet to understand what language models learn during that process. |
| Approach: | They propose diagnostics that ask questions about information used by language models for generating predictions in context. |
| Outcome: | The proposed diagnostics can be used to study the popular BERT model . they show that the model can distinguish good from bad completions, but struggles with inference and role-based event prediction. |
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
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| Challenge: | End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks. |
| Approach: | They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation. |
| Outcome: | The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference. |
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing (2020.tacl-1)
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| Challenge: | Chinese word segmentation and dependency parsing suffer from error propagation . a graph-based model can integrate both tasks, but it suffers from performance limitations . |
| Approach: | They propose a graph-based model to integrate Chinese word segmentation and dependency parsing . their model achieves better performance than previous joint models . |
| Outcome: | The proposed model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
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| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification (2022.naacl-main)
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| Challenge: | Existing few-shot text classification methods often lack labeled data in real-world tasks. |
| Approach: | They propose a meta-learning method that encodes how to attend for given tasks . they evaluate the method on five benchmark datasets and show it is competitive . |
| Outcome: | The proposed method performs better on five benchmark datasets than previous methods on labeled data. |
Improving and Simplifying Template-Based Named Entity Recognition (2023.eacl-srw)
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| Challenge: | Named Entity Recognition (NER) is traditionally approached as a sequence labeling task where a tag is predicted for each token. |
| Approach: | They propose to convert a Named Entity Recognition task into a seq2seq task by generating synthetic sentences using templates. |
| Outcome: | The proposed model outperforms the current state-of-the-art approach in resource-rich, low resource and domain transfer settings and the negative examples play an important role in its performance. |
Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)
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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
| Challenge: | Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered. |
| Approach: | They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model. |
| Outcome: | The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications. |
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
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| Challenge: | Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation. |
| Approach: | They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework. |
| Outcome: | The proposed framework is easy to use and flexible enough to integrate with other frameworks. |
Clinical Text Anonymization, its Influence on Downstream NLP Tasks and the Risk of Re-Identification (2023.eacl-srw)
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| Challenge: | De-identification and anonymization of clinical data is needed to solve access to clinicaldata. |
| Approach: | They propose to use text anonymization techniques to break the anonymization of clinical data . they propose to apply a re-identification attack to the anonymized text data to break this. |
| Outcome: | The proposed approach can break the anonymization of clinical data, the authors show . |
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals (2022.starsem-1)
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| Challenge: | Existing methods to derive sentence embeddings have not been well understood what properties are captured in the resulting sentences depending on the supervision signals. |
| Approach: | They propose to combine two types of sentence embedding methods with similar architectures and tasks to investigate their properties. |
| Outcome: | The proposed methods perform better on unsupervised and downstream tasks than the proposed methods on untrained STS tasks and probing tasks. |
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
RankGen: Improving Text Generation with Large Ranking Models (2022.emnlp-main)
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| Challenge: | Modern language models assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix. |
| Approach: | They propose a 1.2B parameter encoder model for English that scores model generations given a prefix. |
| Outcome: | The proposed model outperforms decoding algorithms on automatic metrics and human evaluations with English writers. |
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies (2023.eacl-main)
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| Challenge: | Existing labeled datasets are heavily imbalanced, limiting the QA performance in this domain. |
| Approach: | They propose a question answering task that captures relevant text segments from unlabeled policy documents and expands the positive examples in the training set. |
| Outcome: | The proposed framework elevates the baseline by a large margin (10% F1) and achieves a new state-of-the-art F1 score of 50%. |
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Existing factual consistency metrics are often uncontrollably generating text that is factually inconsistent with inputs. |
| Approach: | They propose a weakly supervised framework that is directly trained on actual generated samples from language models with weakly annotated labels. |
| Outcome: | The proposed framework improves on the TRUE benchmark by 3.3% over existing methods with 435M parameters. |
Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog (2023.acl-industry)
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Kewei Cheng, Xian Li, Zhengyang Wang, Chenwei Zhang, Binxuan Huang, Yifan Ethan Xu, Xin Luna Dong, Yizhou Sun
| Challenge: | Existing methods for analyzing textual attributes in product catalogs are not effective on structured tabular data since they are trained on free-form natural language texts. |
| Approach: | They propose a model to handle error detection over tabular data following a pre-training paradigm. |
| Outcome: | The proposed model improves on a real-world Amazon Product Catalog table by 16% over state-of-the-art methods and by 11% on PR AUC over attribute value validation task. |
GLTR: Statistical Detection and Visualization of Generated Text (P19-3)
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| Challenge: | GLTR is a tool to detect generated text that can be used by non-experts. |
| Approach: | They propose a tool to detect generated text using a set of statistical methods that can be used by non-experts. |
| Outcome: | The proposed method improves detection rate of fake text from 54% to 72% without training. |
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets (2022.starsem-1)
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| Challenge: | Existing studies on the performance of pre-trained language models on natural language understanding tasks have focused on the natural language inference and textual entailment tasks. |
| Approach: | They propose to use corrupted data to fine-tune pre-trained language models to assess their language understanding capabilities. |
| Outcome: | The proposed transformations can be applied to all but one NLU task and show that understanding the meaning of utterances is not required for high performance. |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
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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. |
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds (2022.naacl-main)
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| Challenge: | Existing topic models adopt a fully unsupervised setting and their discovered topics may not reflect user preferences well due to their unsupervised nature. |
| Approach: | They propose a framework that allows out-of-vocabulary seeds to be used to find latent topics from text corpora. |
| Outcome: | The proposed framework can find topics that are never seen in the corpus and can benefit from the general knowledge of pre-trained language models. |
Can LMs Store and Retrieve 1-to-N Relational Knowledge? (2023.acl-srw)
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| Challenge: | Pretraining language models on large amounts of text has made it difficult to store and retrieve world knowledge. |
| Approach: | They propose to view pretrained language models as knowledge bases by examining their ability to store and retrieve world knowledge. |
| Outcome: | The proposed model can store and retrieve world knowledge with high accuracy, but it is not clear how accurately it can handle 1-to-N relational knowledge. |
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus (2021.acl-short)
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| Challenge: | Recent advances in NLP have been driven by Transformer-based language models. |
| Approach: | They analyze the Common Crawl, a web corpus extensively used for training language models. |
| Outcome: | The Common Crawl contains hate speech and sexually explicit content even after filtering procedures. |
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach (2024.findings-eacl)
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| Challenge: | Large pre-trained language models such as GPT-3.5 and GPT-4 have gained significant attention in natural language research due to limited computational resources or inaccessible parameters. |
| Approach: | They propose a neural programmer-interpreter approach that preserves the domain generalization ability of LLMs while editing their output. |
| Outcome: | The proposed framework significantly improves GPT-3.5’s performance in logical form-to-text conversion and low-resource machine translation, surpassing other state-of-the-art (SOTA) LLM post-editing methods in cross-domain settings. |
MVP-BERT: Multi-Vocab Pre-training for Chinese BERT (2021.acl-srw)
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| Challenge: | Pre-trained language models (PLMs) improve performance of Chinese NLP tasks, but the vocabulary (vocab) for these Chinese PLMs remains to be the one provided by Google Chinese BERT (CITATION), which is based on Chinese characters (chars). |
| Approach: | They propose two versions of multi-vocab pre-training (MVP) to improve Chinese PLMs' expressiveness. |
| Outcome: | The proposed model outperforms the recent AMBERT (CITATION) model on span-level tasks and is more robust against adversarial attacks. |
Compositional Structured Explanation Generation with Dynamic Modularized Reasoning (2024.starsem-1)
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| Challenge: | Large-scale language models have shown remarkable performance on reasoning tasks such as reading comprehension, natural language inference, story generation, etc. |
| Approach: | They propose a compositional structured explanation generation task to test a model's ability to generalize from generating entailment trees to more steps, focusing on the length and shapes of engorgement trees. |
| Outcome: | The proposed model shows competitive compositional generalization abilities in a generation setting. |
SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Science (2024.emnlp-industry)
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| Challenge: | Existing methods to adapt pre-trained language models to materials science rely on frequency information from limited downstream datasets. |
| Approach: | They propose a vocabulary expansion method to adapt pre-trained language models to materials science by incorporating latent materials knowledge of lightweight embeddings into PLMs. |
| Outcome: | The proposed method mitigates the limitations of existing adaptation methods and can be used in materials science. |
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models (2022.findings-aacl)
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| Challenge: | Existing methods do not examine social groups categorised by geographical information, leaving the region-related biases in pre-trained LMs unexplored. |
| Approach: | They propose a hierarchical regional bias evaluation method to quantify regional bias in pre-trained language models. |
| Outcome: | The proposed method evaluates regional bias with regard to comprehensive topics and measures potential regional bias that can be propagated to downstream tasks. |
Diachronic degradation of language models: Insights from social media (P18-2)
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| Challenge: | Existing studies have explored whether and how language models degrade over time, i.e. why they fail to work on contemporary language. |
| Approach: | They investigate the accuracy of pre-trained language models for downstream tasks in machine learning and user profiling. |
| Outcome: | The results show that it is possible to measure diachronic drifts within social media and within the span of a few years. |
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information (2024.naacl-short)
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| Challenge: | Existing approaches to mitigate social biases require explicit annotation of demographic information for each sample. |
| Approach: | They propose a method that leverages predefined demographic texts and incorporates a regularization term during the fine-tuning process to mitigate bias in language models. |
| Outcome: | The proposed method outperforms debiasing methods with limited demographic-annotated data. |
A Static Evaluation of Code Completion by Large Language Models (2023.acl-industry)
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Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta
| Challenge: | Large language models trained on code have shown great potential to increase productivity of software developers. |
| Approach: | They propose a static evaluation framework to quantify static errors in Python code completions by leveraging Abstract Syntax Trees. |
| Outcome: | The proposed framework is more efficient and applicable to code in the wild. |
Choosing What to Mask: More Informed Masking for Multimodal Machine Translation (2023.acl-srw)
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| Challenge: | Pre-trained language models have achieved remarkable results on several NLP tasks. |
| Approach: | They propose three new masking strategies for cross-lingual visual pre-training that focus on learning different linguistic patterns. |
| Outcome: | The proposed methods outperform the baseline model and achieve state-of-the-art accuracy on the Portuguese-English MMT task. |
How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN (2023.tacl-1)
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| Challenge: | Current language models generate high-quality text, but are they copying it or have they learned generalizable linguistic abstractions? |
| Approach: | They propose a suite of analyses for assessing the novelty of generated text . they focus on sequential structure (n-grams) and syntactic structure (syntactical structure). |
| Outcome: | The proposed model-generated text is as novel as the baseline human-generated model- generated text, but it is copied substantially, the authors show . |
Rehearsal-Free Modular and Compositional Continual Learning for Language Models (2024.naacl-short)
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| Challenge: | Existing methods to overcome catastrophic forgetting are rehearsal-based and parameter isolation-based. |
| Approach: | They propose a rehearsal-free framework which continuously adds new modules to language models and composes them with existing modules. |
| Outcome: | Experiments on benchmarks show that MoCL outperforms state-of-the-art and effectively facilitates knowledge transfer. |
BeamR: Beam Reweighing with Attribute Discriminators for Controllable Text Generation (2022.findings-aacl)
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| Challenge: | Recent advances in natural language processing have led to the availability of large pre-trained language models with rich generative capabilities. |
| Approach: | They propose a method to combine generative LMs with attribute discriminators to control different attributes of text generation. |
| Outcome: | The proposed method performs better than existing state-of-the-art approaches in sentiment steering and machine translation formality tasks. |
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods for extracting complete (binary) parses from pre-trained language models are expensive and time-consuming. |
| Approach: | They propose a chart-based method and an effective top-K ensemble technique to extractbinary parses from PLMs. |
| Outcome: | The proposed method can induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, and is robust to cross-lingual transfer. |
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models (2021.emnlp-main)
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| Challenge: | Infrequent names are less similar to initial representations, and are more self-similar, suggesting that models rely on less context-informed representations of uncommon and minority names. |
| Approach: | They use a dataset of U.S. first names with labels based on predominant gender and racial group to examine effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias. |
| Outcome: | The results show that infrequent names are less similar to initial representations and have a Spearman’s rho between frequency and self-similarity as low as .763 . |
Adversarial Scrubbing of Demographic Information for Text Classification (2021.emnlp-main)
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Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, Snigdha Chaturvedi
| Challenge: | Existing frameworks to debias contextual representations can encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated task. |
| Approach: | They propose an adversarial learning framework to debias contextual representations by encoding undesirable attributes while being trained for an unrelated task. |
| Outcome: | The proposed framework debiases representations on 8 datasets while remaining informative on the target task. |
Task-driven Layerwise Additive Activation Intervention (2025.naacl-short)
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| Challenge: | Existing approaches to task adaptation rely heavily on heuristic rules or prompt inputs. |
| Approach: | They propose a layer-wise additive activation intervention framework that steers the LMs’ generation process by identifying and manipulating the activations. |
| Outcome: | The proposed framework improves the accuracy of pretrained LMs and competing baselines on various datasets, demonstrating improvements in the accuracy and sample efficiency of the proposed framework. |
Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models (2023.acl-short)
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| Challenge: | Augmenting language models with a retrieval mechanism has been shown to improve performance while keeping the number of parameters low. |
| Approach: | They propose to augment language models with a retrieval mechanism by replacing semantic retrieval with BM25 . they find that the model's performance is better explained by surface-level similarities, they say . |
| Outcome: | The proposed method reduces perplexity and lowers the number of parameters while keeping the number low. |
Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning (2024.findings-eacl)
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| Challenge: | Existing NLP methods treat morality as binary, ranging from right to wrong. |
| Approach: | They propose to build a pluralist moral sentence embedding space using contrastive learning methods to examine relationships among moral elements. |
| Outcome: | The proposed method shows that pluralism can be captured in an embedding space. |
Personal Information Parroting in Language Models (2026.findings-eacl)
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| Challenge: | Modern language models memorize millions of PI instances, increasing privacy risks. |
| Approach: | They develop a model that parrots 13.6% of PI verbatim on a manually curated set of 483 instances . they recommend that pretraining datasets be aggressively filtered and anonymized to minimize PI parroting. |
| Outcome: | The proposed model outperforms the best regex-based PI detectors on a manually curated set of 483 instances of PI. |
How About Kind of Generating Hedges using End-to-End Neural Models? (2023.acl-long)
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| Challenge: | hedging is a strategy for softening the impact of a statement in conversation. |
| Approach: | They propose to fine-tune state-of-the-art language models trained on human-human tutoring data and then use a hedge classifier to select the candidate that best matches the expected hedging strategy. |
| Outcome: | The proposed model is feasible in a noisy environment with reranking, and it is compared with other approaches. |
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model (2024.emnlp-industry)
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| Challenge: | Existing approaches to named entity recognition are domain specific and require a domain specific architecture. |
| Approach: | They propose a retrieval augmented large language model for Named Entity Recognition . the model uses word-embedding over sentence-level embedding to fine tune . |
| Outcome: | The proposed model outperforms existing models on the CrossNER dataset. |
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)
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| Challenge: | Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models. |
| Approach: | They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples. |
| Outcome: | The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples. |
Improve Language Model and Brain Alignment via Associative Memory (2025.findings-acl)
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| Challenge: | Existing studies have shown that associative memory is essential for language comprehension and comprehension. |
| Approach: | They propose to integrate associative memory into language models to improve alignment . they find alignment is improved in brain regions closely related to associativ memory processing . |
| Outcome: | The proposed model improves in brain regions closely related to associative memory processing. |
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs (2024.acl-long)
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| Challenge: | supervised fine-tuning (SFT) on a limited offline dataset does not yield good performance. |
| Approach: | They propose a two-player system to fine-tune an LM using SFT and online RL . they use negative example generation to enhance error-correction ability of the reflection model . |
| Outcome: | The proposed system outperforms SFT and online RL without reflection on a GPT-2 XL 1.56B model. |
mEdIT: Multilingual Text Editing via Instruction Tuning (2024.naacl-long)
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| Challenge: | mEdIT is a multi-lingual extension to CoEdit for writing assistance. |
| Approach: | They propose to train multi-lingual large language models (LLMs) by fine-tuning them via instruction tuning. |
| Outcome: | The proposed model performs well on multilingual text editing benchmarks and generalizes well to new languages. |
LM-CORE: Language Models with Contextually Relevant External Knowledge (2022.findings-naacl)
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| Challenge: | Large pre-trained language models can capture factual knowledge in their parameters but storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of information and resource requirements. |
| Approach: | They propose a framework that provides explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art knowledge-enhanced language models on knowledge probing tasks and can handle knowledge updates. |
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering (2021.tacl-1)
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| Challenge: | Recent studies have shown that language models capture different types of knowledge regarding facts or commonsense knowledge. |
| Approach: | They examine how language models can be calibrated to make their confidence scores correlate better with the likelihood of correctness. |
| Outcome: | The proposed calibration methods improve confidence scores on QA tasks and improve accuracy. |
KESA: A Knowledge Enhanced Approach To Sentiment Analysis (2022.aacl-main)
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| Challenge: | Recent work on injecting sentiment knowledge into pre-trained language models, but it is difficult to integrate external knowledge into PLMs. |
| Approach: | They propose two sentiment-aware auxiliary tasks to integrate sentiment knowledge into the objective of the downstream task. |
| Outcome: | The proposed tasks outperform baselines and complement existing sentiment-enhanced models. |
Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have revolutionized general natural language preprocessing tasks, but their performance in financial domains is not evaluated comprehensively. |
| Approach: | They propose a framework to evaluate financial language models on financial tasks . they compare performance of auto-encoding language models and ChatGPT . |
| Outcome: | The proposed framework compares the performance of auto-encoding language models and the LLM ChatGPT on financial tasks. |
Beyond Literal Token Overlap: Token Alignability for Multilinguality (2025.naacl-short)
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| Challenge: | Existing studies have shown that token overlap is a strong predictor of multilinguality and cross-lingual knowledge transfer between languages with different scripts. |
| Approach: | They propose a subword token alignability metric to understand the impact and quality of multilingual tokenisation. |
| Outcome: | The proposed metric predicts multilinguality much better when scripts are disparate and the overlap of literal tokens is low. |
On the Emergence and Test-Time Use of Structural Information in Large Language Models (2026.acl-long)
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| Challenge: | a controlled environment is required to study how language models learn structural information from observational data. |
| Approach: | They propose a natural language dataset based on linguistic structural transformations to study how language models learn abstract structures and utilize the learnt structural information at test-time. |
| Outcome: | The proposed model can generate new knowledge outside the training corpus in a controlled environment. |
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data. |
| Approach: | They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy. |
| Outcome: | The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging). |
Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)
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| Challenge: | Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored. |
| Approach: | They propose to use large-scale pre-trained language models to generate an event-level temporal graph from a document using existing IE/NLP tools. |
| Outcome: | The proposed method outperforms the closest existing method on several metrics on a hand-labeled, out-of-domain corpus. |
Can Machine Unlearning Reduce Social Bias in Language Models? (2024.emnlp-industry)
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Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khattak
| Challenge: | Existing methods for mitigating bias in language models are expensive and time-consuming . comparative studies have not evaluated their respective advantages and disadvantages . |
| Approach: | They propose to use Partitioned Contrastive Gradient Unlearning and Negation via Task Vector to reduce social biases in open-source language models. |
| Outcome: | The proposed methods outperform PCGU and DPO in debiasing models . the proposed methods can be easily tuned to balance the trade-off between bias reduction and generation quality . |
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022.acl-long)
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| Challenge: | Existing methods to mitigate human-like biases in pretrained language models are based on external corpora and require a distribution alignment loss to mitigate them. |
| Approach: | They propose an automatic method to mitigate biases in pretrained language models by searching for biased prompts such that cloze-style completions are the most different with respect to different demographic groups. |
| Outcome: | The proposed method reduces biases in pretrained language models, including gender and racial bias, and improves fairness of the models. |
Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning (2025.findings-naacl)
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| Challenge: | Existing studies have shown that large language models implicitly embed reasoning trees, but their internal mechanisms remain largely opaque due to the complexity of non-linear interactions and high-dimensional operations. |
| Approach: | They propose to use circuit analysis and self-influence functions to map the reasoning process of large models. |
| Outcome: | The proposed model is able to map human-interpretable reasoning paths and a model's underlying circuits reveal human-mediated reasoning processes. |
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision. |
| Approach: | They propose to use pre-trained sequence-to-sequence language models to generate graphs . they propose to learn structural constraints and semantics of graphs with limited supervision . |
| Outcome: | The proposed models can learn structural constraints and semantics of graphs with limited supervision. |
“Why do I feel offended?” - Korean Dataset for Offensive Language Identification (2023.findings-eacl)
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| Challenge: | Existing methods for detecting offensive content rely on labeled datasets, but few consider low-resource languages with relatively less data available for training. |
| Approach: | They propose to use Korean as a dataset for offensive language identification . they propose to perform abusive language detection and sentiment analysis to help identify offensive languages. |
| Outcome: | The proposed datasets improve the performance of offensive language identification in Korean, while the existing methods are limited. |
PLM-based World Models for Text-based Games (2022.emnlp-main)
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| Challenge: | a new study shows that pre-trained world models provide a strong base for world models . worldformer is a text-based game environment that can be used to learn world models in text-driven games. |
| Approach: | They propose to use pre-trained language models to build world models in text-based game environments. |
| Outcome: | The proposed model outperforms state-of-the-art model-free algorithms in Atari games while retaining sample efficiency. |
Exploring Decomposition for Table-based Fact Verification (2021.findings-emnlp)
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| Challenge: | Existing research focuses on fact verification based on unstructured text, but structured data is becoming more prevalent. |
| Approach: | They propose to decompose complex statements into simpler subproblems to improve table-based verification by a weakly supervised parser. |
| Outcome: | The proposed method achieves state-of-the-art accuracy on the TabFact benchmark. |
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models (2022.naacl-main)
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| Challenge: | Pre-trained language models encode correlations between social groups and traits, like associating the group with the group. |
| Approach: | They adapt the Agency-Belief-Communion (ABC) stereotype model to a language model and introduce the sensitivity test (SeT) to measure stereotypical associations. |
| Outcome: | The proposed framework is used to measure stereotyping of intersectional identities in language models. |
Event Pattern-Instance Graph: A Multi-Round Role Representation Learning Strategy for Document-Level Event Argument Extraction (2025.findings-acl)
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| Challenge: | Existing role-based span selection strategies ignore interrelations between events . authors propose a multi-round role representation learning strategy for document-level event argument extraction . |
| Approach: | They propose a pattern-instance graph to capture role semantics embedded in various associations . they also propose re-inventing the role representations learned from previous analyzed documents . |
| Outcome: | The proposed model captures role semantics embedded in various associations . iteratively updates representations of role nodes and edges to enrich their semantic information . the model improves prediction performance in subsequent rounds of span selection . |
Detection-Correction Structure via General Language Model for Grammatical Error Correction (2024.acl-long)
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| Challenge: | Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits. |
| Approach: | They propose a detection-correction structure based on the general language model which integrates detection and correction into a single model. |
| Outcome: | The proposed model outperforms the state-of-the-art models on English and Chinese datasets. |
Grounded Compositional Outputs for Adaptive Language Modeling (2020.emnlp-main)
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| Challenge: | Language models are a key component of natural language processing, but their size is a problem because they are typically trained with a closed output vocabulary derived from the training data. |
| Approach: | They propose a fully compositional output embedding layer for language models that is grounded in semantically related words and free-text definitions. |
| Outcome: | The proposed model outperforms state-of-the-art methods and adaptation approaches on cross-domain modeling and cross-learning tasks. |
Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)
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| Challenge: | Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization. |
| Approach: | They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks. |
| Outcome: | The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks. |
Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting (2023.acl-short)
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| Challenge: | Existing studies addressing gender bias of pre-trained language models, usually build a small gender-neutral data set and conduct a second phase pre-training with such data. |
| Approach: | They propose a method to improve gender fairness of pre-trained models with less forgetting by evaluating them with general NLP tasks in GLUE. |
| Outcome: | The proposed method improves gender fairness of pre-trained models with less forgetting and performs better on GLUE by a large margin. |
In-Context Analogical Reasoning with Pre-Trained Language Models (2023.acl-long)
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| Challenge: | Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. |
| Approach: | They apply large pre-trained language models to visual Raven’s Progressive Matrices (RPM) and use language-based abstractions to support analogy in AI systems. |
| Outcome: | The proposed language-based abstractions outperform human models on Raven’s Progressive Matrices and supervised vision-based methods. |
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering (2023.acl-long)
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| Challenge: | Among recent NLP research, multi-document processing is gaining increasing attention due to the need to handle and process an increasing amount of textual data and available documents online. |
| Approach: | They propose to pre-train a generic multi-document model from a cross-document question answering pre-training objective by generating salient sentences from one document and challenging it to recover the sentence from which it was generated. |
| Outcome: | The proposed model outperforms zero-shot GPT-3.5 and GPT-4 in multiple document tasks and generates the correct answer and the salient sentence from a salient document. |
Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
| Outcome: | The proposed methods improve translation and summarization by 6.9% and 7.5% respectively. |
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (2022.emnlp-main)
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Eric Mitchell, Joseph Noh, Siyan Li, Will Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, Christopher Manning
| Challenge: | Existing frameworks for boosting consistency and accuracy of pre-trained NLP models without fine-tuning or re-training are lacking. |
| Approach: | They propose a framework for boosting the consistency and accuracy of pre-trained NLP models using pre-trainer natural language inference models without fine-tuning or re-training. |
| Outcome: | The proposed framework boosts consistency and accuracy of pre-trained NLP models using pre-train natural language inference models without fine-tuning or re-training. |
Cognitive Biases, Task Complexity, and Result Intepretability in Large Language Models (2025.coling-main)
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| Challenge: | Recent work shows that cognitive biases occur frequently in language models . a cognitive bias is a systematic deviation in judgment that simplifies complex decisions . |
| Approach: | They evaluate the performance of different groups of models for each type of cognitive bias . they find that task complexity plays a part in eliciting stronger effects for some biases . |
| Outcome: | The proposed models perform better for each type of bias in different settings . the results show that task complexity plays a part in eliciting stronger effects . |
Pruning Adatperfusion with Lottery Ticket Hypothesis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are computationally expensive to fine-tune and require large storage. |
| Approach: | They propose a method to identify the influence of each adapter module and a way to prune adapters based on the Lottery Ticket Hypothesis. |
| Outcome: | The proposed model reduces size significantly while keeping performance intact. |
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency (2022.acl-long)
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| Challenge: | Structured pruning has been extensively studied on monolingual pre-trained models . but little attention has been paid to evaluating the effectiveness of structured pruning on multilingual models. |
| Approach: | They investigate settings, algorithms, and efficiency of structured pruning on multilingual models . authors propose a simple approach that allows training the model once and adapting to different model sizes at inference . |
| Outcome: | The proposed approach allows training the model once and adapting to different model sizes at inference. |
Seeing All Sides: Multi-Perspective In-Context Learning for Subjective NLP (2026.findings-eacl)
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| Challenge: | Modern language models excel at factual reasoning but struggle with value diversity, authors say . task-sensitive tasks such as hate speech expose this limitation . human disagreement captures the diversity of plausible human perspectives, authors argue . |
| Approach: | They evaluate four large language models with human disagreements on five datasets . they find multi-perspective in-context learning outperforms standard prompting . |
| Outcome: | The proposed approach outperforms standard prompting on English labels while disaggregated soft predictions better align with human judgments in Arabic and Italian datasets. |
A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have succeeded in natural language processing because they learn generic knowledge from a large corpus. |
| Approach: | They propose a method that allows pre-trained language models to explore simile knowledge from PLMs . they enhance PLM models with a multi-level simile recognition task that evaluates similes aplenty . |
| Outcome: | The proposed method can explore more accurate simile knowledge for PLMs. |
Efficient Benchmarking (of Language Models) (2024.naacl-long)
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Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen
| Challenge: | Efficient Benchmarking is a method for reducing computation costs of LM evaluation without compromising reliability. |
| Approach: | They propose to reduce the computation costs of LM evaluation without compromising reliability by using a new measure - Decision Impact on Reliability. |
| Outcome: | The proposed benchmarks reduce computation costs by x100 or more, while maintaining reliability. |
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (2022.emnlp-main)
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| Challenge: | a new method for textual style transfer is proposed for text with a limited set of style choices . textual styles are a complex task that requires specialized models to perform . |
| Approach: | They propose a method for arbitrary textual style transfer using pre-trained language models . they use a mathematical formulation of the TST task, decomposing it into three components . |
| Outcome: | The proposed method performs on par with state-of-the-art large-scale models while using less compute and memory. |
AVATAR: A Parallel Corpus for Java-Python Program Translation (2023.findings-acl)
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| Challenge: | Program translation is a time-consuming and costly process that requires expertise in both the source and target languages. |
| Approach: | They present a collection of 9,515 programming problems and their solutions written in Java and Python. |
| Outcome: | The proposed model lacks in generating functionally accurate code. |
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data (P18-1)
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| Challenge: | Code-mixed (CM) language training is a difficult problem because of lack of data and the increased confusability due to the presence of more than one language. |
| Approach: | They propose a computational technique for creating grammatically valid artificial CM data based on the Equivalence Constraint Theory. |
| Outcome: | The proposed method reduces the perplexity of the model and does not reduce the perceptibility of the models. |
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction (2022.findings-naacl)
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| Challenge: | Temporal Expression Extraction (TEE) is essential for understanding time in natural language. |
| Approach: | They propose a framework for multilingual Temporal Expression Extraction that leverages pre-trained language models to prompt cross-language knowledge transfer from English to non-English languages. |
| Outcome: | The proposed framework outperforms the existing SOTA methods on French, Spanish, Portuguese, and Basque by large margins. |
Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on DSTC7 and DSTF8* with competitive results on UbuntuV2 . |
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models (2022.findings-naacl)
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| Challenge: | Recent studies show that the energy requirements of current NLP models are growing at a rapid, unsustainable pace. |
| Approach: | They investigate ways to measure energy usage and different hardware settings that can be tuned to reduce energy consumption for training and inference for language models. |
| Outcome: | The proposed techniques can reduce energy consumption for training and inference for language models. |
On Context Utilization in Summarization with Large Language Models (2024.acl-long)
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| Challenge: | Large language models excel in abstractive summarization tasks, delivering fluent and pertinent summaries. |
| Approach: | They conduct the first comprehensive study on context utilization and position bias in summarization. |
| Outcome: | The proposed benchmark compares two methods to alleviate position bias in summarization tasks. |
Gradient-based Intra-attention Pruning on Pre-trained Language Models (2023.acl-long)
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| Challenge: | Pre-trained language models are computationally expensive and slow in inference due to their large sizes. |
| Approach: | They propose a structured pruning method which combines pruning with knowledge distillation to yield highly effective models. |
| Outcome: | The proposed method outperforms other pruning methods in sparsity regimes while maintaining 93% 99% performance. |
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)
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| Challenge: | Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training. |
| Approach: | They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning. |
| Outcome: | Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing. |
Training Dynamics for Text Summarization Models (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets. |
| Approach: | They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process . |
| Outcome: | The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens . |
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation (2022.findings-naacl)
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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
| Challenge: | Korean pretrained language models struggle to generate short sentences with a given condition based on compositionality and commonsense reasoning. |
| Approach: | They propose a Korean text-generation dataset for Korean generative commonsense reasoning and language model evaluation using a semi-automatic dataset construction approach. |
| Outcome: | The proposed dataset is available at http://aihub.or.kr/opendata/korea-university. |
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling (2022.emnlp-main)
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| Challenge: | Existing studies on dialogue modeling use pre-trained language models to encode dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. |
| Approach: | They propose a bidirectional information decoupling network as a universal dialogue encoder which explicitly incorporates both the past and future contexts. |
| Outcome: | The proposed model incorporates past and future contexts and can be generalized to a wide range of dialogue-related tasks. |
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data. |
| Approach: | They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve table’s structural information. |
| Outcome: | The proposed model outperforms existing systems on most few-shot settings. |
Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)
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| Challenge: | Existing work on word alignment has focused on unsupervised learning on parallel text. |
| Approach: | They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs. |
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2022.findings-naacl)
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| Challenge: | Existing approaches to answer open domain questions rely on unlabeled text or synthetically generated question-answer pairs. |
| Approach: | They propose a large-scale open-domain question-answering dataset based on the Common Crawl project that can be used to in-domain pre-train popular language models. |
| Outcome: | The proposed dataset achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks. |
Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension (2023.findings-eacl)
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| Challenge: | Existing high-quality xMRC datasets can be further utilized to fine-tune our model. |
| Approach: | They propose a cross-lingual question answering over knowledge base approach that converts KB subgraphs into passages to narrow the gap between KB schemas and questions. |
| Outcome: | The proposed approach outperforms baselines and achieves strong few-shot and zero-shot performance on two xKBQA datasets in 12 languages. |
BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types (2022.findings-emnlp)
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Syed Mohammed Sartaj Ekram, Adham Arik Rahman, Md. Sajid Altaf, Mohammed Saidul Islam, Mehrab Mustafy Rahman, Md Mezbaur Rahman, Md Azam Hossain, Abu Raihan Mostofa Kamal
| Challenge: | a lack of diverse and comprehensive question-answering datasets exists in under-resourced languages like Bangla. |
| Approach: | They propose a reading comprehension-based Bangla question-answering dataset . the dataset includes answerable and unanswerable questions covering four categories of questions . |
| Outcome: | The proposed dataset shows that it performs well as a training resource in high-resource languages. |
SP3: Enhancing Structured Pruning via PCA Projection (2024.findings-acl)
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| Challenge: | Structured pruning is a widely used technique for reducing the size of pre-trained language models, but current methods overlook the potential of compressing the hidden dimension d in PLMs. |
| Approach: | They propose a structured pruning approach that projectes features into a space defined by principal components before masking the hidden dimension d in pre-trained language models. |
| Outcome: | Experiments on benchmarks show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, and maintain over 96% accuracy. |
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws (2024.emnlp-main)
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| Challenge: | Existing quality filtering methods rely on a high-quality dataset as reference . Existing methods introduce potential biases and compromise diversity . |
| Approach: | They propose a method that evaluates text quality based on the perplexity difference between two language models trained on the same data. |
| Outcome: | The proposed approach improves performance of pre-trained models without increasing training costs. |
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks (2021.findings-emnlp)
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| Challenge: | Existing methods to generate pre-trained language models with attributes are expensive and overfitted on small training sets. |
| Approach: | They propose a novel approach to control the generation of Transformer-based pre-trained language models using a new control attributes loss framework. |
| Outcome: | The proposed method is shown to perform well with very limited training samples. |
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach (2025.coling-main)
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| Challenge: | Using language models (LMs) has increased in use, and the use of biases and stereotypes is creating social problems. |
| Approach: | They propose a method to mitigate LM biases by continual training on biased data . they use masked language modeling to construct a Bias Vector as the difference between biased LMs and pre-trained LM weights . |
| Outcome: | The proposed method improves on the GLUE and SEAT benchmarks. |
Syntax-guided Contrastive Learning for Pre-trained Language Model (2022.findings-acl)
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| Challenge: | Existing studies rely on additional syntax-driven attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks. |
| Approach: | They propose a syntax-guided contrastive learning method which does not change the transformer architecture and does not alter the transformer structure. |
| Outcome: | The proposed method achieves consistent improvements in a variety of tasks including grammatical error detection, entity tasks, structural probing and GLUE. |
High-quality argumentative information in low resources approaches improve counter-narrative generation (2023.findings-emnlp)
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| Challenge: | a recent study shows that fine-tuning improves the performance of language models . large language models generate acceptable texts in a number of scenarios, a study shows . |
| Approach: | They show that fine-tuning improves the task of hate speech counter-narrative generation . they provide a subset of arguments and a good base model is required for the fine-uning to have a positive impact. |
| Outcome: | The proposed model produces counter-narratives that are as satisfactory as the whole set. |
Conformity in Large Language Models (2025.acl-long)
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| Challenge: | Conformity is a form of social influence that affects the way people respond to information. |
| Approach: | They adapt psychological experiments to examine the extent of conformity in large language models. |
| Outcome: | The proposed interventions mitigate conformity by reducing the naturalness of majority tones and reducing instruction-tuned models. |
Do dialogue representations align with perception? An empirical study (2023.eacl-main)
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| Challenge: | masked language models produce stronger correlations than auto-regressive models, but humans and models make different response selection mistakes. |
| Approach: | They propose to use spoken conversation as a model to measure human comprehension behaviour. |
| Outcome: | The proposed model outperforms the model which produces the strongest correlation with human responses. |
Parameter-efficient Modularised Bias Mitigation via AdapterFusion (2023.eacl-main)
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Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz
| Challenge: | Large pre-trained language models contain societal biases and carry along these biase . Current approaches to mitigate these bias impose debiasing by updating model parameters, effectively transferring model to irreversible debiased state. |
| Approach: | They propose to develop stand-alone debiasing functionalities separate from the model, which can be integrated into the model on-demand while keeping the core model untouched. |
| Outcome: | The proposed approach improves or maintains effectiveness of bias mitigation, avoids catastrophic forgetting in a multi-attribute scenario, and maintains on-par task performance while granting parameter-efficiency and easy switching between the original and debiased models. |
Tree-Based Representation and Generation of Natural and Mathematical Language (2023.acl-long)
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| Challenge: | Existing models for generating and modeling mathematical language are limited . existing models for modeling and generating mathematical language simply treat mathematical expressions as text . |
| Approach: | They propose to combine mathematical expressions and text-based models to generate mathematically valid expressions. |
| Outcome: | The proposed model outperforms baselines on mathematical expression generation tasks. |
Selective Differential Privacy for Language Modeling (2022.naacl-main)
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| Challenge: | Existing methods to protect sensitive data from leaking are over-pessimistic and undifferentiated. |
| Approach: | They propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility. |
| Outcome: | The proposed privacy-preserving mechanism achieves better utility while remaining safe under various privacy attacks compared to baselines. |
A Zero-Shot Monolingual Dual Stage Information Retrieval System for Spanish Biomedical Systematic Literature Reviews (2024.naacl-long)
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| Challenge: | Existing studies have shown that most SRs are skewed towards English databases, excluding databases in Languages other than English (LoE). |
| Approach: | They propose a zero-shot dual information retrieval baseline system that integrates traditional retrieval methods with pre-trained language models and cross-attention re-rankers for enhanced accuracy in Spanish biomedical literature retrieval. |
| Outcome: | The proposed system improves on three real-life case studies in Spanish biomedical literature retrieval using the LILACS database, which is known for its coverage of Latin American and Caribbean biomedically literature. |
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods focus on single editing, failing to meet the requirements for lifelong editing. |
| Approach: | They propose an approach that selects editing layer based on the pattern matching degree of editing knowledge across different layers in language models. |
| Outcome: | The proposed method improves on GPT2-XL and GPT-J in lifelong editing compared to state-of-the-art methods . |
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
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| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
StyLEx: Explaining Style Using Human Lexical Annotations (2023.eacl-main)
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| Challenge: | Large pre-trained language models often learn spurious domain-specific words to make predictions. |
| Approach: | They propose a model that learns from human annotated explanations of stylistic features and jointly predicts them as model explanations. |
| Outcome: | The proposed model can provide human like stylistic lexical explanations without sacrificing performance on in-domain and out-of-domain datasets. |
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
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| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
| Approach: | They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. |
| Outcome: | The proposed method surpasses well established transfer learning methods with greater level of complexity on a variety of affective and text classification tasks surpassing well established methods with higher level of difficulty. |
Mitigating Paraphrase Attacks on Machine-Text Detection via Paraphrase Inversion (2025.findings-acl)
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| Challenge: | Paraphrases applied to machine-generated texts can degrade performance of machine-text detectors. |
| Approach: | They propose an approach which frames the problem as translation from paraphrased text back to the original text. |
| Outcome: | The proposed approach yields an average improvement of +22% AUROC across seven detectors and three different domains. |
Adapting Language Models to Compress Contexts (2023.emnlp-main)
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| Challenge: | Transformer-based language models have a finite context window and expensive computational cost of processing long text documents. |
| Approach: | They propose to adapt pre-trained LMs into AutoCompressors to compress text into summary vectors . authors propose to use summary vector to speed up inference over long contexts based on a finite context window . |
| Outcome: | The proposed model can compress long contexts into summary vectors, which are accessible as soft prompts. |
Evaluating Text GANs as Language Models (N19-1)
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| Challenge: | Generative Adversarial Networks (GANs) do not suffer from the problem of exposure bias. |
| Approach: | They propose to approximate the distribution of text generated by a GAN and compare it to traditional probability-based LM metrics. |
| Outcome: | The proposed method performs significantly worse than state-of-the-art LMs on several GAN-based models and can accelerate progress in GAN text generation. |
Parameter-Efficient Fine-Tuning without Introducing New Latency (2023.acl-long)
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| Challenge: | Parameter-efficient fine-tuning of pre-trained language models has been demonstrated to be effective, but its inherent characteristics limit its performance. |
| Approach: | They propose to generate a sparse mask in a task-agnostic manner by modifying only a small subset of existing parameters and adding new parameters. |
| Outcome: | The proposed method surpasses existing methods on the GLUE benchmark by a significant margin. |
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation (2023.findings-emnlp)
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| Challenge: | Conversational Recommendation System (CRS) is a rapidly growing research area, along with advancements in language modelling techniques. |
| Approach: | They propose to use a benchmark dataset to develop CRS models and address biases arising from feedback loop inherent in multi-turn interactions to enhance model performance while mitigating biase. |
| Outcome: | The proposed strategies improve on ReDial and TG-ReDial benchmark datasets and offer additional insights on addressing multiple newly formulated biases. |
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering (2024.findings-acl)
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Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Vijay Prakash Dwivedi, Stefan Winkler
| Challenge: | Existing studies on adapting large language models to perform a variety of tasks in high-stakes domains such as healthcare lack understanding of the extent and contributing factors that allow them to recall relevant knowledge and combine it with presented information. |
| Approach: | They propose to use multiple choice and abstractive question answering to investigate the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with presented information in the clinical and biomedical domain. |
| Outcome: | The proposed models perform better on 22 datasets in three generalist and three specialist biomedical sub-domains, and show that they can generalise to unseen sub- domains. |
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)
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| Challenge: | Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications. |
| Approach: | They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers. |
| Outcome: | The proposed method can learn from different teacher layers adaptively for different NLP tasks. |
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification (2021.findings-acl)
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| Challenge: | Existing studies focus on building a meta-learner from input text but ignore abundant semantic information beneath class labels. |
| Approach: | They propose a framework to make full use of label semantics in few-shot text classification systems. |
| Outcome: | The proposed framework can be plugged into the existing few-shot text classification system. |
Analyzing Semantic Change through Lexical Replacements (2024.acl-long)
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| Challenge: | Modern language models can contextualize words based on their surrounding contexts, but semantic change can compromise this capability. |
| Approach: | They propose a replacement schema where a target word is replaced with lexical replacements of varying relatedness . they leverage the replacement schema as a basis for a novel interpretable model for semantic change . |
| Outcome: | The proposed model is the first to evaluate LLaMa for semantic change detection . it shows that lexical replacements can detect unexpected contexts . |
Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (2021.acl-long)
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| Challenge: | Existing work is limited in using small benchmarks with high test-train overlaps. |
| Approach: | They construct a dataset of closed-book QA using SQuAD and investigate the performance of BART. |
| Outcome: | Experiments show that pre-trained language models can achieve high performance on closed-book QA tasks. |
DORE: Document Ordered Relation Extraction based on Generative Framework (2022.findings-emnlp)
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| Challenge: | Existing generative methods do not fit document-level relation extraction tasks where there are multiple entities and relational facts. |
| Approach: | They propose to generate a symbolic and ordered sequence from relation matrix which is easier to learn and introduce several negative sampling strategies to improve the performance with balanced signals. |
| Outcome: | The proposed method can improve the performance of the generative DocRE models on four datasets. |
Open-ended Knowledge Tracing for Computer Science Education (2022.emnlp-main)
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| Challenge: | Knowledge tracing (KT) is a method used to estimate student mastery of concepts/skills/knowledge components from their responses to questions and to predict future performance. |
| Approach: | They propose a student knowledge-guided code generation approach that combines program synthesis methods with student knowledge tracing methods to solve the OKT problem. |
| Outcome: | The proposed method is based on a student knowledge-guided code generation approach and validates on coding questions. |
Enhancing Language Representation with Constructional Information for Natural Language Understanding (2023.acl-long)
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| Challenge: | Recent advances in natural language processing focus on acquiring lexico-semantic information. |
| Approach: | They propose a construction grammar which highlights the pairings of form and meaning to enrich language representation. |
| Outcome: | The proposed model is superior to existing models on a variety of NLU tasks. |
Benchmarking Intersectional Biases in NLP (2022.naacl-main)
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| Challenge: | Recent work on fairness of machine learning models has focused on how to debias, but research on the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. |
| Approach: | They assess intersectional bias - fairness across multiple demographic dimensions . they highlight possible causes and make recommendations for future NLP debiasing research. |
| Outcome: | The proposed approaches fare well in terms of fairness-accuracy trade-off, but are unable to effectively alleviate bias in downstream tasks. |
CogBERT: Cognition-Guided Pre-trained Language Models (2022.coling-1)
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| Challenge: | Existing methods fine-tune pre-trained models on cognitive data, ignoring the semantic gap between texts and cognitive signals. |
| Approach: | They propose a framework that can induce fine-grained cognitive features from cognitive data and incorporate them into pre-trained language models by adaptively adjusting the weight of cognitive features for different NLP tasks. |
| Outcome: | The proposed framework can induce fine-grained cognitive features from cognitive data and incorporate them into BERT by adaptively adjusting weight of cognitive features for different NLP tasks. |
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
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| Challenge: | Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text. |
| Approach: | They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. |
| Outcome: | The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers. |
Increasing Robustness to Spurious Correlations using Forgettable Examples (2021.eacl-main)
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| Challenge: | Neural NLP models often exploit spurious correlations to perform tasks. minority examples have been shown to increase the out-of-distribution generalization of pre-trained language models. |
| Approach: | They propose to use example forgetting to find minority examples without prior knowledge of spurious correlations in the dataset. |
| Outcome: | The proposed approach improves out-of-distribution generalization on minorities . it shows that minority examples are more robust on challenging datasets . |
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models (2025.coling-main)
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| Challenge: | Existing methods for creating a vision question-answering with natural language explanations rely on human annotations that are time-consuming and costly. |
| Approach: | They propose a method that generates high-quality natural language explanations using LVLMs by using visual prompts. |
| Outcome: | The proposed method generates high-quality synthetic VQA-NLE datasets 20x faster than human annotations with minimal decrease in qualitative metrics. |
Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints (2023.findings-acl)
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| Challenge: | Existing work has not evaluated LNMT models under challenging real-world conditions. |
| Approach: | They propose a homograph disambiguation module and a model that integrates contextually rich information about unseen lexical constraints from pre-trained language models. |
| Outcome: | The proposed model can cope with “homographs” and “unseen” lexical constraints. |
Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)
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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu
| Challenge: | Existing studies show that Large Language Models can be misused to generate undesired content. |
| Approach: | They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs. |
| Outcome: | The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs. |
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions (N19-1)
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| Challenge: | In this paper we build a reading comprehension dataset of yes/no questions that are naturally occurring . they often query for complex, non-factoid information, and require difficult entailment-like inference to solve. |
| Approach: | They build a reading comprehension dataset of yes/no questions that are naturally occurring . they find they are unexpectedly challenging and require difficult inferences to solve . |
| Outcome: | The proposed method achieves 80.4% accuracy compared to 90% accuracy of human annotators and 62% majority-baseline. |
That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context? (2023.findings-emnlp)
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| Challenge: | Existing models for translation of ambiguous text use context to disambiguate meaning . current models for MTs consistently translate English idioms literally, whereas LMs are context-aware . |
| Approach: | They use a dataset of 512 pairs of English sentences to study semantic ambiguities . they use literal and figurative idioms to disambiguate intended meaning . |
| Outcome: | The results show that current models translate English idioms literally, even when the context suggests a figurative interpretation. |
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models (2021.emnlp-main)
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| Challenge: | Recent named entity recognition models have great performance on many conventional benchmarks, but it is not reliable in realistic applications. |
| Approach: | They propose a method to create natural adversarial examples using Wikidata and pre-trained language models. |
| Outcome: | The proposed method produces natural adversarial examples with a shifted distribution from training data. |
Korean Language Modeling via Syntactic Guide (2022.lrec-1)
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| Challenge: | Existing research on pre-trained language models focuses on widely-used languages . however, not every language can benefit from such models due to computational resources . |
| Approach: | They propose to build a pre-trained language model that understands the linguistic phenomena in the target language with low resources. |
| Outcome: | The proposed model improves the performance of Korean language understanding tasks. |
ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing (2023.emnlp-main)
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| Challenge: | English and Chinese have seen the strong development of transformer-based language models for natural language processing tasks. |
| Approach: | They present a monolingual pre-trained language model for Vietnamese social media texts . they explore emotion recognition, hate speech detection, sentiment analysis, spam reviews detection . |
| Outcome: | The proposed model outperforms the existing models on Vietnamese social media tasks with fewer parameters. |
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer (2022.findings-acl)
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| Challenge: | Existing work has focused on what is captured by multi-modal architectures. |
| Approach: | They propose a multi-modal transformer that learns syntactic and semantic representations about entities and relations grounded in objects at the level of masked self-attention and cross-modal attention. |
| Outcome: | The proposed model learns syntactic and semantic representations about objects and relations cross-modally and unimodally. |
Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases (2024.lrec-main)
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| Challenge: | linguists have suggested that some languages are "cooler" than others because of their contexts. |
| Approach: | They propose to omit plurality and definiteness markers in Chinese noun phrases . they build a corpus of Chinese NPs accompanied by its context . |
| Outcome: | The proposed model predicts the plurality and definiteness of Chinese noun phrases (NPs) it shows that speakers drop plurality markers very frequently, and that they are more likely to drop pronouns . |
Supervised Seeded Iterated Learning for Interactive Language Learning (2020.emnlp-main)
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| Challenge: | Recent work has focused on word-based conversational agents that tend to invent their language rather than leveraging natural language. |
| Approach: | They propose two methods to counter language drift by combining S2P and Seeded Iterated Learning to minimize their weaknesses. |
| Outcome: | The proposed methods reduce late-stage training collapses and higher negative likelihood when evaluated on human corpus. |
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
| Outcome: | The proposed model-generated stories are based on a commonsense reasoning engine and are able to uncover gender biases in the protagonist's motivations, attributes, mental states, and implications on others. |
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning (2025.findings-naacl)
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Hyundong Justin Cho, Karishma Sharma, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Jonathan May, Alessandro Moschitti
| Challenge: | Language models are biased towards generic outputs as they are trained to align to an aggregate preference to be generally useful. |
| Approach: | They propose a tuning-free method that personalizes language models for text generation tasks with fewer than 10 examples per user. |
| Outcome: | The proposed method achieves favorable win rates on pairwise comparisons with the previous state-of-the-art and outperforms competitive tuning-free baselines for personalized alignment tasks of writing emails, essays and news articles. |
Word Frequency Does Not Predict Grammatical Knowledge in Language Models (2020.emnlp-main)
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| Challenge: | Neural language models learn the grammatical properties of natural languages to varying degrees of accuracy. |
| Approach: | They focus on subject-verb agreement and reflexive anaphora to investigate whether there are systematic sources of variation in the language models’ accuracy. |
| Outcome: | The proposed model can learn grammatical properties from training data. |
BERT-like Models for Slavic Morpheme Segmentation (2025.acl-long)
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| Challenge: | Existing morpheme segmentation algorithms for Slavic languages have been improved but performance is still low for words with roots not present in training data. |
| Approach: | They propose to fine-tune BERT-like models for morpheme segmentation using data from Belarusian, Czech, and Russian to account for word semantics. |
| Outcome: | The proposed models outperform all previous approaches in Czech and Russian, with word-level accuracy of 92.5-95.1%. |
DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (2023.findings-acl)
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| Challenge: | Prompt-based methods have shown their efficacy in transferring general knowledge within pre-trained language models (PLMs) however, when applied to zero-shot entity and relation extraction, they struggle with the limited coverage of verbalizers to labels and the slow inference speed. |
| Approach: | They propose a method which reformulates zero-shot tasks into token discrimination tasks without having to construct verbalizers. |
| Outcome: | The proposed method outperforms baselines on two zero-shot entity recognition datasets with higher inference speed and achieves 7.5% improvement over previous state-of-the-art models on Wiki-ZSL and FewRel. |
ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) requires high-quality labeled datasets. |
| Approach: | They propose a method that uses pre-trained language models to generate entity-controlled text to augment small labeled datasets for downstream NER tasks. |
| Outcome: | The proposed method produces full text samples with desired entities appearing in a controllable way while retaining sentence coherence closest to the real world data. |
Cards Against Contamination: TCG-Bench for Difficulty-Scalable Multilingual LLM Reasoning (2026.findings-eacl)
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| Challenge: | Recent studies find 25-50% of evaluation datasets appear in training corpora . contamination hinders the possibility to differentiate memorization and reasoning skills. |
| Approach: | They propose a two-player trading card game that is contaminated by a public engine and hidden card implementations to prevent benchmark saturation. |
| Outcome: | The proposed benchmark is based on a new two-player trading card game similar to Magic: The Gathering. |
Enhancing Hierarchical Text Classification through Knowledge Graph Integration (2023.findings-acl)
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| Challenge: | Existing approaches to hierarchical text classification are limited by lack of domain knowledge, which leads to mistakes in a variety of situations. |
| Approach: | They propose a Knowledge-enabled Hierarchical Text Classification model which integrates knowledge graphs into HTC to address the knowledge limitations of traditional methods. |
| Outcome: | The proposed model integrates knowledge graphs into the hierarchical text classification process, addressing the knowledge limitations of traditional methods. |
Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
| Approach: | They propose a framework to transfer commonsense knowledge stored in a neural commonsensing model to a general-purpose pre-trained language model. |
| Outcome: | Empirical results show that the proposed framework improves the model’s performance on downstream tasks that require commonsense reasoning. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
Montague semantics and modifier consistency measurement in neural language models (2025.coling-main)
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| Challenge: | Existing studies on distributional language models have been focused on linguistics and their relationship with semantic formalisms for decades. |
| Approach: | They propose a method for measuring compositional behavior in contemporary language embedding models by introducing three new tests inspired by Montague semantics. |
| Outcome: | The proposed method measures compositional behavior in language embedding models on adjectival modifier phenomena in adjective-noun phrases. |
All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality (2021.emnlp-main)
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| Challenge: | Similarity measures are a vital tool for understanding how language models represent and process language. |
| Approach: | They propose to use cosine similarity and Euclidean distance to understand how words cluster in semantic space. |
| Outcome: | The proposed measures show that rogue dimensions dominate similarity measures and reveal representational quality. |
Unsupervised Commonsense Question Answering with Self-Talk (2020.emnlp-main)
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| Challenge: | Current systems rely on pre-trained language models or external knowledge bases to incorporate additional relevant knowledge. |
| Approach: | They propose an unsupervised framework based on self-talk to improve commonsense performance by asking language models to ask information seeking questions. |
| Outcome: | Empirical results show that the proposed framework improves on four out of six commonsense benchmarks and competes with models that obtain knowledge from external KBs. |
Measuring and Narrowing the Compositionality Gap in Language Models (2023.findings-emnlp)
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| Challenge: | a language model can correctly answer all sub-problems but not generate the overall solution. |
| Approach: | They propose a method that asks itself and then answers follow-up questions to narrow the compositionality gap by reasoning explicitly instead of implicitly. |
| Outcome: | The proposed method improves on chain of thought by asking itself and answering follow-up questions. |
Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election (2025.coling-main)
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Roberto Mondini, Neema Kotonya, Robert L Logan IV, Elizabeth M. Olson, Angela Oduor Lungati, Daniel Odongo, Tim Ombasa, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Systematically organizing and geotagging large amounts of crowdsourced information requires substantial manual effort, often led by volunteers. |
| Approach: | They present a dataset of 14k citizen reports related to the 2022 Kenyan General Election . they investigate whether language models can assist in scalably categorizing and geotagging reports . |
| Outcome: | The proposed dataset aims to show whether language models can assist in categorizing and geotagging reports, thus highlighting its potential application in the AI for Social Good space. |
Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)
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| Challenge: | Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size. |
| Approach: | They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally. |
| Outcome: | The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval. |
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification (2022.lrec-1)
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| Challenge: | Recent advances in natural language processing (NLP) have reshaped the industry . complexity of such models makes them a “black box” and can cause ethical concerns . |
| Approach: | They propose a convolutional TM architecture that breaks down text into a sequence of fragments . they propose to use a tokenization scheme to bind the tokens to the text fragments. |
| Outcome: | The proposed architecture improves on a set of text fragments and eliminates the need for a corpus-specific vocabulary. |
From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)
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| Challenge: | Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'. |
| Approach: | They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs. |
| Outcome: | The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels. |
Probing Simile Knowledge from Pre-trained Language Models (2022.acl-long)
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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. |
Construction Artifacts in Metaphor Identification Datasets (2023.emnlp-main)
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| Challenge: | Existing metaphor identification datasets can be gamed by completely ignoring the potential metaphorical expression or the context in which it occurs. |
| Approach: | They show that existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. |
| Outcome: | The proposed system can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. |
Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
| Outcome: | The proposed method is able to induce effective graph structures for input texts to boost representation learning. |
Identifying Semantic Induction Heads to Understand In-Context Learning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance, but lack of transparency in their inference logic raises concerns about their trustworthiness. |
| Approach: | They conduct a detailed analysis of the operations of attention heads to understand their in-context learning of LLMs. |
| Outcome: | The proposed analysis of attention heads reveals that they increase the output logits of object tokens and recall objects . the proposed model is a novel approach to understand the in-context learning of large language models. |
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)
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| Challenge: | Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead. |
| Approach: | They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data. |
| Outcome: | The proposed approach outperforms current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs on three datasets and greatly reduces the huge size of training data. |
Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)
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| Challenge: | Existing temporal language models are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components. |
| Approach: | They propose a method that captures syntactically changed tokens and captures the relationship between the time prefix and tokens. |
| Outcome: | The proposed method outperforms existing temporal language models on two datasets and three tasks. |
Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models (2025.findings-naacl)
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| Challenge: | Language models (LMs) have significant potential for clinical prediction tasks . however, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns . |
| Approach: | They propose to combine ensembling and multi-tasking approaches to reduce uncertainty in EHRs by using multi-tapping methods. |
| Outcome: | The proposed framework reduces model uncertainty in white-box and black-box settings, and improves model transparency in both settings. |
Detecting Cybercrimes in Accordance with Pakistani Law: Dataset and Evaluation Using PLMs (2024.lrec-main)
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| Challenge: | Roman Urdu is a widely used language in Pakistan but lacks sufficient resources and tools for text-based cybercrime detection. |
| Approach: | They propose to use a benchmark dataset for text-based cybercrime detection in Roman Urdu to improve the performance of pre-trained language models. |
| Outcome: | The proposed model achieves the highest performance on all metrics. |
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)
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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi
| Challenge: | Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited. |
| Approach: | They propose an inference-time policy adapter which tailors a large base model without fine-tuning it. |
| Outcome: | The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4. |
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
| Approach: | They propose a lexical-level masking strategy to post-train a neural language model using static data from past years. |
| Outcome: | The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets. |
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model (2022.findings-emnlp)
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| Challenge: | Existing approaches to generate cloze distractors with carefully-designed distractors are limited due to wrong option selection. |
| Approach: | They propose to employ pre-trained language models as an alternative to cloze distractor generation by using pre-designed distractors. |
| Outcome: | The proposed model improves the state-of-the-art cloze test score from 14.94 to 34.17 (NDCG@10) The proposed framework improves clozing distractors by incorporating pre-trained language models. |
How transfer learning impacts linguistic knowledge in deep NLP models? (2021.findings-acl)
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| Challenge: | Several researchers have shown that deep NLP models learn non-trivial amount of linguistic knowledge, captured at different layers of the model. |
| Approach: | They propose to fine-tune pre-trained models towards downstream NLP tasks to capture linguistic knowledge. |
| Outcome: | The proposed model is adapted to GLUE tasks and retains linguistic information in the network while forgetting it. |
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)
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| Challenge: | Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance. |
| Approach: | They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
| Outcome: | The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for video-grounded dialogue generation do not allow information from different modalities to complement each other. |
| Approach: | They propose a video-grounded dialogue generation model that integrates video data into pre-trained language models to allow information from different modalities to complement each other. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic and human evaluations on two public datasets. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. |
| Approach: | They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. |
| Outcome: | The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge. |
Explore Unsupervised Structures in Pretrained Models for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Syntactic trees are widely used in relation extraction (RE) but they are not stable on different text domains and a pre-defined grammar may not fit the target relation schema. |
| Approach: | They propose to use unsupervised structures to extract relation extraction models . they also conduct detailed analyses on their abilities of adapting new RE domains . |
| Outcome: | The proposed models obtain competitive (even the best) performance scores on benchmark RE datasets. |
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Prompt Tuning has been successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks. |
| Approach: | They propose to use a vector-quantized input-contextualized prompt as an extension to the soft prompt tuning framework to learn contextualization of soft prompt tokens. |
| Outcome: | The proposed prompt outperforms soft prompt tuning by an average margin of 1.19% on various language understanding tasks like SuperGLUE, QA, Relation classification, NER and NLI. |
Transformers in the loop: Polarity in neural models of language (2022.acl-long)
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| Challenge: | Recent Transformer-based language representation models (LRMs) show impressive results on practical text analysis tasks, but do they have access to complex linguistic notions? |
| Approach: | They propose to use polarity as a case study to compare metrics derived from language models to human judgments obtained in psycholinguistic experiments. |
| Outcome: | The proposed model is more accurate than linguistic theory predictions for polarity, and allows us to use language models to discover new insights into natural language grammar beyond existing linguistic theories. |
Copyright Violations and Large Language Models (2023.emnlp-main)
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| Challenge: | a recent study examines the extent to which language models can memorize training data . a fair use exemption to copyright laws allows for limited use of copyrighted material . |
| Approach: | They examine the extent to which language models can redistribute copyrighted text . they use a range of popular books and coding problems to study copyright violations . |
| Outcome: | This study examines the extent to which language models can redistribute copyrighted text . it shows that language models may memorize entire chunks of training data . |
Modeling Noise in Paraphrase Detection (2022.lrec-1)
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| Challenge: | Noisy labels in training data are challenging and can lead to incorrect decisions . large pre-trained language models have achieved great results in many NLP tasks . |
| Approach: | They propose to use a linear noise model to augment pre-trained language models to account for label noise in fine-tuning. |
| Outcome: | The proposed model can be applied without further knowledge about annotation quality and label confidence of training examples and their results are compared with other models. |
Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition (2025.acl-long)
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| Challenge: | Large language models possess general linguistic abilities comparable to humans but their efficiency in language acquisition remains far inferior. |
| Approach: | They propose a method that initially constrains working memory during the early stages of training and gradually relaxes this constraint as learning progresses. |
| Outcome: | The proposed method outperforms conventional methods without memory constraints or with static memory constraints. |
Language Model Quality Correlates with Psychometric Predictive Power in Multiple Languages (2023.emnlp-main)
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| Challenge: | Existing studies have found that higher quality language models provide more powerful predictors of human reading behavior, but empirical support for the QP hypothesis is mixed. |
| Approach: | They propose to test the quality–power hypothesis by using surprisal language models to test their ability to predict eye tracking data. |
| Outcome: | The proposed model is based on a set of language models with a 'quality-power' hypothesis. |
A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models (2024.naacl-long)
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| Challenge: | Psychologists have documented several ways in which humans’ inferences deviate from the rules of logic. |
| Approach: | They focus on syllogisms, which are inferences from two simple premises, and show that larger models are more logical than smaller ones. |
| Outcome: | The results show that language models often mimic human biases, but overcome them in some cases. |
Evaluation of HTR models without Ground Truth Material (2022.lrec-1)
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Phillip Benjamin Ströbel, Martin Volk, Simon Clematide, Raphael Schwitter, Tobias Hodel, David Schoch
| Challenge: | Optical Character Recognition (OCR) is a well-established technique for digitising historical printed collections in libraries and archives. |
| Approach: | They propose to use masked language models to evaluate handwritten text recognition models . they propose to introduce GT-free metrics to evaluate models to ensure best results . |
| Outcome: | The proposed model evaluations are based on lexicon-based and masked language models. |
Close or Cloze? Assessing the Robustness of Large Language Models to Adversarial Perturbations via Word Recovery (2025.coling-main)
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| Challenge: | Existing models implicitly recover the original text, but it is unclear when they rely on context and when they implicitly do so. |
| Approach: | They propose to use a dictionary to recover adversarial words by using a phonetic, typo, and visual attack to study word recovery performance. |
| Outcome: | The proposed model outperforms open-source models on hateful, offensive, and toxic classification tasks. |
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index (2025.coling-main)
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| Challenge: | Prompt engineering is a growing subdiscipline of natural language processing . a lack of appropriate consideration for the financial constraints of computationally burdensome methods can limit their adoption and impact. |
| Approach: | They propose a new metric that combines accuracy scores with token consumption to reflect different resource constraints. |
| Outcome: | The economic prompting index (EPI) measures the performance of 6 prompting techniques across 10 widely-used language models and 4 diverse datasets. |
A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)
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| Challenge: | This survey provides the first in-depth review of multilingual reasoning in Language Models. |
| Approach: | This survey provides the first in-depth review of multilingual reasoning in LMs. |
| Outcome: | The present study provides the first in-depth review of multilingual reasoning in LMs. |
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
Pre-trained Language Models for Entity Blocking: A Reproducibility Study (2024.naacl-long)
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| Challenge: | Entity Resolution (ER) is an essential task in data integration . state-of-the-art neural IR models that are based on large language models have not been evaluated on the ER task. |
| Approach: | They evaluate state-of-the-art neural IR models that are based on large language models on a wide range of real-world datasets and evaluate their generalization abilities. |
| Outcome: | The proposed methods have been evaluated on a wide range of datasets and their generalization abilities. |
Towards Pareto-Efficient RLHF: Paying Attention to a Few High-Reward Samples with Reward Dropout (2024.findings-emnlp)
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| Challenge: | RLHF is a bi-objective problem that has the nature of a Pareto optimization . reward dropout is generalizable and most effective with non-pretrained target models . |
| Approach: | They propose a method that guarantees a Pareto improvement by leveraging reinforcement learning to fine-tune language models. |
| Outcome: | The proposed method guarantees a Pareto improvement on two benchmark datasets . it is generalizable and most effective with non-pretrained target models, saving the effort of pretraining. |
M3Seg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in ASR Transcripts (2023.emnlp-main)
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| Challenge: | Topic segmentation aims to split automatic speech recognition transcriptions into segments that are bounded by thematic meanings. |
| Approach: | They propose a Maximum-Minimum Mutual information paradigm for linear topic segmentation without using any parallel data. |
| Outcome: | The proposed paradigm outperforms the state-of-the-art methods by a significant margin. |
Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA (2021.emnlp-main)
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| Challenge: | Taking the exam closed book, but having read the textbook, yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misundersttoo the questions). |
| Approach: | They propose to use pre-trained language models to answer questions from introductory college textbooks and hundreds of true/false statements based on review questions written by the authors. |
| Outcome: | The proposed task includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History). |
Toward A Digital Twin of U.S. Congress (2026.findings-acl)
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| Challenge: | a virtual model of congresspersons based on a collection of language models meets the definition of a digital twin. |
| Approach: | They propose to use a daily-updated dataset to generate tweets from congresspersons . they show that a modern language model equipped with subsets of this dataset produces Tweets that are indistinguishable from actual Tweets posted by their physical counterparts. |
| Outcome: | The proposed model produces Tweets that are indistinguishable from actual tweets posted by congresspersons. |
ReasonBERT: Pre-trained to Reason with Distant Supervision (2021.emnlp-main)
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| Challenge: | Existing pre-training methods only harvest learning signals from local contexts of naturally occurring texts . ReasonBert provides a method for reasoning over long-range relations and multiple, possibly hybrid contexts. |
| Approach: | They propose a method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. |
| Outcome: | The proposed method significantly improves sample efficiency over strong baselines. |
Impact of Adversarial Training on Robustness and Generalizability of Language Models (2023.findings-acl)
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| Challenge: | Adversarial training is widely acknowledged as the most effective defense against adversarial attacks, but achieving both robustness and generalization requires a trade-off. |
| Approach: | They propose to compare pre-training data augmentation and training time input perturbations with embedding space perturbations to find out whether they improve generalization. |
| Outcome: | The proposed methods improve generalization and robustness of the trained models. |
Crossing Variational Autoencoders for Answer Retrieval (2020.acl-main)
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| Challenge: | Existing methods learned semantic representations with dual encoders or dual variational auto-encoders failed to capture the aligned semantics between question and answer. |
| Approach: | They propose to use two variational auto-encoders to generate questions with aligned answers and generating answers with align questions. |
| Outcome: | The proposed method outperforms the state-of-the-art answer retrieval method on SQuAD. |
HOLMS: Alternative Summary Evaluation with Large Language Models (2020.coling-main)
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| Challenge: | Efficient document summarization requires evaluation measures that can rank a set of systems based on an average score and highlight which individual summary is better than another. |
| Approach: | They propose a hybrid evaluation measure for document summarization called HOLMS that combines both language models pre-trained on large corpora and lexical similarity measures. |
| Outcome: | The proposed measure outperforms ROUGE and BLEU on several extractive summarization datasets for both linguistic quality and pyramid scores. |
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing (2022.findings-emnlp)
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| Challenge: | Existing discourse parsing methods need a strong baseline for reporting reliable experimental results. |
| Approach: | They integrate existing parsing strategies with transformer-based pre-trained language models to provide a strong baseline for reporting reliable experimental results. |
| Outcome: | The proposed model outperforms the current best model using DeBERTa. |
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation (2022.coling-1)
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| Challenge: | Recent improvements in KG-to-text generation are due to additional pre-training tasks . these tasks require extensive computational resources while only suggesting marginal improvements. |
| Approach: | They propose a mask structure to capture neighborhood information and a type encoder that adds a bias to the graph-attention weights depending on the connection type. |
| Outcome: | The proposed model outperforms state-of-the-art models while requiring no additional pre-training tasks. |
On the Relationship between Truth and Political Bias in Language Models (2024.emnlp-main)
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Suyash Fulay, William Brannon, Shrestha Mohanty, Cassandra Overney, Elinor Poole-Dayan, Deb Roy, Jad Kabbara
| Challenge: | Language model alignment research often attempts to ensure that models are helpful and harmless, but can obscure how improving one aspect might impact the other. |
| Approach: | They analyze the relationship between truthfulness and political bias in language models. |
| Outcome: | The results show that optimizing models for truthfulness results in a left-leaning political bias. |
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning (2025.naacl-long)
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| Challenge: | Existing methods for ensembling language models fail to address complex reasoning tasks. |
| Approach: | They propose a framework for process-level ensembling of large language models using Monte Carlo tree search. |
| Outcome: | The proposed framework outperforms both language model decoding and language model ensemble methods on five reasoning benchmarks. |
Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed. |
| Approach: | They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content. |
| Outcome: | The proposed method outperforms existing language models in combating adversarial attacks in Chinese content. |
Language Model Augmented Relevance Score (2021.acl-long)
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| Challenge: | Existing metrics that compare the candidate with the human reference do not consider the context, resulting in poor correlation with human judgements. |
| Approach: | They propose a language model-aware metric that augments the human reference while considering the context to provide evaluation scores that correlate highly with human judgements. |
| Outcome: | The proposed metric achieves higher correlation with human reference judgements and differentiates well-formed candidates from adversarial samples to a larger degree. |
LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles (2025.findings-emnlp)
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Ho Yin Sam Ng, Edward Hsu, Aashish Anantha Ramakrishnan, Branislav Kveton, Nedim Lipka, Franck Dernoncourt, Dongwon Lee, Tong Yu, Sungchul Kim, Ryan A. Rossi, Ting-Hao Kenneth Huang
| Challenge: | Figure captions are crucial for helping readers understand and remember a figure’s key message. |
| Approach: | They propose a dataset for personalized figure caption generation with multimodal figure profiles that provide inputs and profiles for each figure . |
| Outcome: | The proposed dataset provides inputs and profiles for personalized figure caption generation with multimodal figure profiles. |
Evaluating Language Translation Models by Playing Telephone (2025.emnlp-main)
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| Challenge: | Existing language models are inadequate for evaluating machine translation systems . current evaluation methods are costly and require specialized expertise to prepare and score gold standard translations . |
| Approach: | They propose an unsupervised method to generate training data for translation evaluation by repeated rounds of translation between source and target languages. |
| Outcome: | The proposed method outperforms a popular translation evaluation system on two tasks . human annotation is costly and requires specialized expertise to prepare and score gold standard translations . |
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation (2024.lrec-main)
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| Challenge: | Existing methods to augment textual data are limited due to the discrete characteristics of the textual dataset. |
| Approach: | They propose a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models by shifting latent features closer to the decision boundary and reconstruction to generate an ambiguous version with a soft label. |
| Outcome: | The proposed method performs better than existing methods and is extensible with curriculum data augmentation. |
Learning to Imagine: Visually-Augmented Natural Language Generation (2023.acl-long)
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| Challenge: | Existing methods for natural language generation are pre-trained on text-only corpora, resulting in visual commonsense. |
| Approach: | They propose a method that makes pre-trained language models learn to imagine for visually-augmented natural language generation. |
| Outcome: | The proposed method is compatible with Transformer-based architecture. |
Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary (2023.emnlp-main)
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| Challenge: | Existing studies have exploited data augmentation or implemented specialised loss functions to alleviate the inconsistent behaviour issue, but they consume expensive training resources and can only handle a certain consistency type. |
| Approach: | They propose a method that allows PLMs to capture accurate meaning by learning precise interrelationships between concepts from word-definition pairs in a dictionary. |
| Outcome: | The proposed method can improve multiple types of consistency and integrate pre-trained knowledge with PLMs’ pre-training knowledge. |
Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank (2024.lrec-main)
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| Challenge: | Existing methods for lightweight fine-tuning are ineffective in low-resource settings but fail in high-resourced settings, leading to unreliable outcomes. |
| Approach: | They propose a calibration strategy that takes into account the inherent variance of generalization ability in model components and potential changes during the fine-tuning process. |
| Outcome: | The proposed calibration improves GLUE score by 3.1 points over the previous calibration method. |
SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)
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| Challenge: | Sentiment analysis models often fail to capture the broader complexities of sentiment analysis. |
| Approach: | They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews . |
| Outcome: | The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% . |
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)
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| Challenge: | Recent pre-trained language models have achieved remarkable zero-shot performance . we propose a self-learning framework that utilizes unlabeled data of target languages . |
| Approach: | They propose a self-learning framework that utilizes unlabeled data of target languages to select silver labels for cross-lingual transfer tasks. |
| Outcome: | The proposed framework outperforms baseline models on two cross-lingual tasks by 10 F1 on average and 2.5 accuracy on natural language inference (NLI). |
Can Pre-trained Language Models Interpret Similes as Smart as Human? (2022.acl-long)
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| Challenge: | Simile interpretation is a crucial task in natural language processing. |
| Approach: | They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions. |
| Outcome: | The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans. |
EsCoLA: Spanish Corpus of Linguistic Acceptability (2024.lrec-main)
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| Challenge: | Acceptability is one of the general language understanding evaluation benchmarks (GLUE) probing tasks . EsCoLA consists of 11,174 sentences and their acceptability judgements as found in well-known Spanish reference grammars. |
| Approach: | They propose to use a corpus of linguistic acceptability (ESCoLA) EsCoLA consists of 11,174 sentences and their acceptability judgements . |
| Outcome: | The proposed task is based on 11,174 sentences and their acceptability judgements as found in well-known Spanish reference grammars. |
Feature Interactions Reveal Linguistic Structure in Language Models (2023.findings-acl)
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| Challenge: | Existing features attribution methods for post-hoc interpretability ignore the existence of interactions between the effects of features on the prediction. |
| Approach: | They propose a grey box method to train models to perfection on a formal language classification task using PCFGs. |
| Outcome: | The proposed methods are able to uncover the grammatical rules acquired by the model under specific configurations and provide novel insights into the linguistic structure of the target models. |
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions (2023.acl-long)
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| Challenge: | Recent work on why Transformer-based large language models make predictions has made their behavior opaque due to the complexity of the computations performed within each layer. |
| Approach: | They propose a linear decomposition of final hidden states from autoregressive language models based on each initial input token, which is exact for virtually all contemporary Transformer architectures. |
| Outcome: | The proposed method analyzes the influence of input tokens on model probabilities over a sequence of upcoming words with only one forward pass from the model. |
Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)
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| Challenge: | Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words. |
| Approach: | They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously. |
| Outcome: | The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset. |
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)
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| Challenge: | Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure. |
| Approach: | They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible. |
| Outcome: | The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model. |
On the Effect of (Near) Duplicate Subwords in Language Modelling (2024.findings-acl)
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| Challenge: | Tokenisation is a core part of language models but can lead to less efficient training because it removes character-level information. |
| Approach: | They propose to use a tokenisation method to split a character sequence into subwords which are assigned random indices before being served to the LM. |
| Outcome: | The proposed model can generalise across duplicated subwords, but this incurs extra cost and is less data efficient. |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (D19-1)
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| Challenge: | Pretrained language models require unlabelled data for training, while cross-lingual models underperform on low-resource languages. |
| Approach: | They propose a multi-lingual language model fine-tuning to train and fine- tune language models efficiently in their own language. |
| Outcome: | The proposed method outperforms existing models on two widely used datasets on cross-lingual classification tasks. |
World Models for Math Story Problems (2023.findings-acl)
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| Challenge: | Recent efforts to solve math story problems have lacked accurate representations of mathematical concepts. |
| Approach: | They propose a graph-based semantic formalism for solving math story problems . they combine existing datasets and annotate a corpus of 1,019 problems with MathWorld . |
| Outcome: | The proposed model can be used to solve math story problems with pre-trained language models . the model can also be used for generating new problems by using the model as a design space . |
ACENet: Attention Guided Commonsense Reasoning on Hybrid Knowledge Graph (2022.emnlp-main)
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| Challenge: | Existing approaches estimate plausibility of candidate choices separately based on their respective KGs, without considering the interference among different choices. |
| Approach: | They propose an Attention guided Commonsense rEasoning Network to integrate hybrid knowledge into the neural network. |
| Outcome: | The proposed model outperforms existing methods on CommonsenseQA and OpenbookQA datasets and shows significant performance gains. |
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided Decoding (2024.emnlp-main)
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| Challenge: | Existing methods to calibrate language models are limited in inference-time efficiency or fail to provide informative signals. |
| Approach: | They propose an activation-based calibration method, ActCab, which trains a linear layer on top of the LM’s last-layer activations. |
| Outcome: | The proposed method improves on five popular QA benchmarks and reduces the average expected calibration error (ECE) score by up to 39%. |
Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages (2024.acl-long)
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Shih-Cheng Huang, Pin-Zu Li, Yu-chi Hsu, Kuang-Ming Chen, Yu Tung Lin, Shih-Kai Hsiao, Richard Tsai, Hung-yi Lee
| Challenge: | Despite the rapid development of large language models, the language capabilities of most open-source LLMs are primarily focused on English due to data constraints. |
| Approach: | They propose a chat vector to equip pre-trained language models with instruction following and human value alignment via simple model arithmetic. |
| Outcome: | The proposed method can be extended to include various languages, base models, and chat vectors. |
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)
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| Challenge: | Existing methods for metaphor interpretation are slow due to lack of annotated datasets and effective pre-trained language models. |
| Approach: | They propose a large annotated dataset and a PLM for the metaphor interpretation task. |
| Outcome: | The proposed method improves on metaphor identification and interpretation with comparable baselines on the new dataset. |
On the Similarity of Circuits across Languages: a Case Study on the Subject-verb Agreement Task (2024.findings-emnlp)
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| Challenge: | Several algorithms implemented by language models have been successfully reverse-engineered, leaving it unclear how universal circuits are across different settings. |
| Approach: | They propose to use Gemma 2B to solve the subject-verb agreement task across two different languages, English and Spanish. |
| Outcome: | The proposed circuits solve the subject-verb agreement task across two different languages, and are language-independent and language-dependent. |
Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs (2024.acl-long)
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| Challenge: | Currently, language models struggle to generate commonsense inferences for complex tasks due to data scarcity and the difficulty of reasoning over multiple pieces of information. |
| Approach: | They propose a dataset to generate commonsense inferences from commonsensible data . they use a commonsence knowledge graph to extract and form questions from existing commonseense knowledge graphs. |
| Outcome: | The proposed dataset improves the ability of language models to reason about complex events without expensive human annotations. |
Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)
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| Challenge: | Existing methods to model event associations struggle with semantic ambiguity and embedding bias. |
| Approach: | They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model . |
| Outcome: | The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism. |
TalkUp: Paving the Way for Understanding Empowering Language (2023.findings-emnlp)
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| Challenge: | Empowerment has rarely been studied in NLP because of its implicit nature . linguistics and psychology research shows how empowerment can impact people by increasing their sense of self-efficacy and self-esteem. |
| Approach: | They crowdsource Reddit posts labeled for empowerment and use it to train language models that capture empowering and disempowering language. |
| Outcome: | The proposed dataset can be used to train language models that capture empowering and disempowering language. |
Learning Task Decomposition to Assist Humans in Competitive Programming (2024.acl-long)
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| Challenge: | Using language models (LMs) to solve complex problems, humans might struggle to understand and repair flawed ones. |
| Approach: | They propose to automatically decompose complex problems into simpler pieces that correspond to specific subtasks and measure their assistive value. |
| Outcome: | The proposed method enables non-experts to solve 33.3% more problems and speeds them up by 3.3x . |
Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025.coling-main)
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| Challenge: | Existing approaches to detect fake news in unseen domains are limited by domain-specific training. |
| Approach: | They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations . |
| Outcome: | The proposed method can detect fake news in unseen domains with the help of pre-trained language models. |
Exploring Graph Representations of Logical Forms for Language Modeling (2025.findings-acl)
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| Challenge: | Graph-based formal-logical distributional semantics models are more data-efficient than textual counterparts. |
| Approach: | They propose a pretrained language model over graph representations of logical forms as a proof-of-concept. |
| Outcome: | The proposed model outperforms textual, transformer LMs on downstream tasks . the model is likely to scale with additional parameters and pretraining data . |
Revisiting Pruning vs Quantization for Small Language Models (2025.findings-emnlp)
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| Challenge: | Compressing Small Language Models (SLMs) is particularly suited for resource-constrained devices, but their compression dynamics remain underexplored compared to Large Language Model (LLMs). |
| Approach: | They evaluated post-training pruning and quantization methods across six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks. |
| Outcome: | The proposed methods outperform pruning and quantization on six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks. |
Red Teaming Language Models for Processing Contradictory Dialogues (2024.emnlp-main)
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| Challenge: | a recent study shows that language models are prone to self-contradiction during dialogues. |
| Approach: | They propose a red teaming framework that detects and attempts to explain dialogues, then modifies existing contradictory content using the explanation. |
| Outcome: | The proposed task improves the ability to detect contradictory dialogues and provides valid explanations. |
NEWTON: Are Large Language Models Capable of Physical Reasoning? (2023.findings-emnlp)
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| Challenge: | Large Language Models have been shown to encapsulate syntactic, semantic, word sense, and common-sense knowledge, but limited exploration of their physical reasoning abilities has been conducted. |
| Approach: | They propose a repository and benchmark to evaluate LLMs' physical reasoning skills . they use a pipeline to generate a variant of the benchmark customized to the objects and attributes relevant for their application. |
| Outcome: | The proposed benchmark examines the reasoning capabilities of language models across reasoning tasks. |
GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering (2025.findings-acl)
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| Challenge: | Question answering systems have been used for various domains and languages. |
| Approach: | They propose a novel approach for question answering (QA) that combines a dataset of Romanian legal questions with a CROL corpus of laws. |
| Outcome: | The proposed approach achieves competitive results with generally accepted state-of-the-art methods and even exceeds them in most settings. |
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation (2025.coling-main)
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| Challenge: | Generative large language models generate a high-dimensional probability distribution over all tokens in their vocabulary. |
| Approach: | They conduct extensive sensitivity analyses to determine how hyperparameter choices shape the outputs of generative large language models. |
| Outcome: | The proposed methods influence the distribution of diversity and coherence metrics in human-written text, but the optimal configurations vary across models and tasks. |
Effective Fine-Tuning Methods for Cross-lingual Adaptation (2021.emnlp-main)
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| Challenge: | Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. |
| Approach: | They propose a co-tuning method that aims to learn more generalized semantic equivalences when the languages are structurally dissimilar. |
| Outcome: | The proposed method improves on cross-lingual inference and review tasks by capturing the semantic relationship in the parallel data when a few translation pairs are available. |
Gendered Grammar or Ingrained Bias? Exploring Gender Bias in Icelandic Language Models (2024.lrec-main)
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| Challenge: | Large language models are trained on vast datasets and exhibit increased output quality in proportion to the amount of data that is used to train them. |
| Approach: | They explore whether language models mirror gender distributions within professions or exhibit biases tied to their grammatical genders. |
| Outcome: | The proposed model may reflect and amplify gender bias, racism, religious prejudice, and queerphobia in training data that may not always be recent. |
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages (2021.emnlp-main)
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| Challenge: | A study of multilingual fine-tuning yields better performance on downstream NLP applications . low resource languages such as Oriya and Punjabi are found to be the largest beneficiaries of multi-lingual fine tuning. |
| Approach: | They propose to leverage the relatedness of languages that belong to the same family in NLP models by multilingual fine-tuning. |
| Outcome: | The proposed approach improves performance on downstream NLP tasks by 15% compared to monolingual fine-tuning. |
BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs (2025.findings-acl)
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| Challenge: | a core part of legal work that has been underexplored in Legal NLP is the writing and editing of legal briefs. |
| Approach: | They propose to use large language models to help legal professionals with writing briefs by capturing and evaluating their abilities in language models. |
| Outcome: | The proposed tasks show that the models perform well on arguments summarization, argument completion, and case retrieval tasks. |
AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification (2025.findings-emnlp)
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| Challenge: | drafting a patent application is expensive and time-consuming, making it a prime candidate for automation. |
| Approach: | a new framework automates the process of drafting a patent application . the framework decomposes drafting into manageable subtasks . |
| Outcome: | a new framework outperforms existing baselines on drafting patent specification tasks. |
Unsupervised Domain Clusters in Pretrained Language Models (2020.acl-main)
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| Challenge: | Existing methods to select domains from large corpus of data are often over-simplistic and vague. |
| Approach: | They propose to use pre-trained language models to learn sentence representations that cluster by domains without supervision. |
| Outcome: | The proposed methods outperform established methods on domain selection and precision and recall with respect to an oracle selection. |
Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models (2024.findings-acl)
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| Challenge: | Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. |
| Approach: | They propose to calibrate language models to better represent outcome uncertainty . they propose to use two methods to calibrated small open-source models . |
| Outcome: | The proposed fine-tuning strategies can calibrate smaller open-source models to beat pre-trained models 10x their size. |
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022.emnlp-main)
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| Challenge: | Recent pre-trained language models have produced impressive results, but there is still a gap between human written texts and machine-generated outputs. |
| Approach: | They propose a multi-task training strategy for long text generation grounded on the cognitive theory of writing. |
| Outcome: | The proposed model achieves better results on three open-ended generation tasks than baselines. |
Understanding In-Context Learning via Supportive Pretraining Data (2023.acl-long)
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| Challenge: | In-context learning (ICL) is a form of learning that provides a handful of examples at inference time, but it is not well understood why it emerges as the model has never been specifically trained on such demonstrations. |
| Approach: | They adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL and compare it with random subsets of pretrain data. |
| Outcome: | The proposed method improves the model's ICL ability by 18% if it is continued on a small subset of pretraining data. |
Recyclable Tuning for Continual Pre-training (2023.findings-acl)
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Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded. |
| Approach: | They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance . |
| Outcome: | The proposed method improves the convergence and performance of the upgraded PLM. |
To Err Is Human, but Llamas Can Learn It Too (2024.findings-emnlp)
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| Challenge: | Specifically, we fine-tune Llama 2 LMs for error generation and find that this approach yields synthetic errors akin to human errors. |
| Approach: | They propose to fine-tune Llama 2 LMs for error generation and train GEC Llma models using these artificial errors. |
| Outcome: | The proposed approach outperforms state-of-the-art models with gains ranging between 0.8 and 6 F0.5 points across all languages tested. |
Analysis of Plan-based Retrieval for Grounded Text Generation (2024.emnlp-main)
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| Challenge: | Large, parametric language models (LLMs) produce fluent text for many applications . hallucinations are generation of text that is factually correct and semantically plausible . |
| Approach: | They propose to use learning-tuned LLMs to infuse models with retrieval mechanisms to reduce hallucinations. |
| Outcome: | The proposed approach reduces the frequency of hallucinations by reducing the coverage of relevant facts and generating more informative responses while providing higher attribution rates. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
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| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
Concept-aware Data Construction Improves In-context Learning of Language Models (2024.findings-acl)
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| Challenge: | Recent work curating in-context learners assumes that ICL emerges from vast over-parametrization or the scale of multitask training. |
| Approach: | They propose a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. |
| Outcome: | The proposed framework makes it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations and fares comparably to previous in-context learners trained in large-scale multitask learning requiring magnitudes of more training data. |
On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Pre-trained contextual representations like BERT have been widely used for NLP tasks. |
| Approach: | They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective. |
| Outcome: | The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks. |
Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)
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| Challenge: | a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance . |
| Approach: | They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance . |
| Outcome: | The proposed model can quantify subjectivity in cognitive appraisal using fine-tuned models and prompt-based large language models. |
Pre-training Language Models for Comparative Reasoning (2023.emnlp-main)
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| Challenge: | Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions. |
| Approach: | They propose a framework to pre-train language models for enhancing comparative reasoning abilities . they collect scalable data for text-based entity comparison . |
| Outcome: | The proposed framework significantly improves comparative reasoning abilities under low-resource conditions on downstream tasks. |
LM vs LM: Detecting Factual Errors via Cross Examination (2023.emnlp-main)
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| Challenge: | Modern language models (LMs) generate inconsistent, non-attributable or factually incorrect text, which hinders their usability. |
| Approach: | They propose a factuality evaluation framework for LMs that is based on cross-examination to detect inconsistencies between LM and examiner. |
| Outcome: | The proposed framework outperforms existing methods and baselines on factual claims on four benchmarks. |
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper (2023.findings-emnlp)
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| Challenge: | Several platforms currently rate news outlets according to their political bias, but they are not ready for use in the near future. |
| Approach: | They use ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations along with automatically extracted topic annotations. |
| Outcome: | The proposed model can identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. |
AUTOSUMM: Automatic Model Creation for Text Summarization (2021.emnlp-main)
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Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
| Challenge: | Recent efforts to develop deep learning models for text generation tasks are challenging for non-experts. |
| Approach: | They propose methods to automatically create deep learning models for extractive and abstractive summarization tasks using large language models. |
| Outcome: | The proposed methods achieve near state-of-the-art performance on a range of datasets. |
BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification (2022.lrec-1)
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| Challenge: | Large-scale monolingual pre-trained language models have shown promising results for high-resource as well as lowresource languages, especially for text classification. |
| Approach: | They provide a set of recommendations for using pre-trained models for Sinhala text classification and introduce new annotated datasets useful for future research. |
| Outcome: | The proposed models are far superior to existing models for Sinhala and set a strong baseline for text classification when fine-tuned. |
Large Language Models Are Reasoning Teachers (2023.acl-long)
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| Challenge: | Recent studies have shown that chain-of-thought (CoT) prompting can elicit language models to solve complex reasoning tasks step-by-step. |
| Approach: | They propose a method that uses large model samples as reasoning teachers to fine-tune smaller models. |
| Outcome: | The proposed method outperforms prompt-based methods and the teacher model in many tasks and extends it by leveraging the teacher's ability to generate multiple rationales for each original sample. |
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions (2024.findings-emnlp)
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Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth Daly, David Piorkowski, John Richards
| Challenge: | Modern language models exhibit some inherent shortcomings, particularly in conversational settings. |
| Approach: | They propose a set of maxims for describing effective human-AI conversation that include quantity, quality, relevance, manner, benevolence, and transparency. |
| Outcome: | The proposed maxims are applied to human-AI interactions and are based on extensive research from the social science and AI communities. |
Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling (2024.findings-acl)
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| Challenge: | Pre-trained language models excel in natural language understanding (NLU) tasks. |
| Approach: | They propose to apply layer-dependent removal of the causal mask (CM) during LLM fine-tuning to improve SL performance. |
| Outcome: | The proposed approach outperforms state-of-the-art SL models on IE tasks, while achieving state- of-the art results is unclear. |
Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks (2024.findings-acl)
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| Challenge: | Memorisation in neural models is concerned due to overfitting and privacy concerns . a dominant hypothesis based on image classification is that lower layers learn generalisable features and deeper layers specialise and memorise. |
| Approach: | They apply 4 techniques to localise and edit models' memories. |
| Outcome: | The proposed method shows that memorisation is a gradual process rather than a localised one. |
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning (2026.findings-acl)
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| Challenge: | Large reasoning models have performance enhancements but still suffer from shortcomings due to limitations of the underlying language models. |
| Approach: | They propose a framework that allows the model to choose when to trust or ignore the tool results based on the confidence score of generated code blocks. |
| Outcome: | The proposed framework reduces the "Tool Ignored" issue by 4.1% to 7.5%. |
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)
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| Challenge: | Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality. |
| Approach: | They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use. |
| Outcome: | The proposed model can generalize to IEs unseen during training. |
Diffusion Guided Language Modeling (2024.findings-acl)
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| Challenge: | Existing guidance methods for text generation are prone to decoding errors and degrade performance. |
| Approach: | They propose a model that steers an auto-regressive language model to generate text with desired properties. |
| Outcome: | The proposed model outperforms existing guidance methods on a wide range of benchmark data sets. |
HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)
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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. |
| Approach: | They propose a hybrid alignment framework **HAF-RM** that incorporates additional constraint on token-level policy probabilities in addition to the reward score. |
| Outcome: | The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models (2024.findings-acl)
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| Challenge: | Language models (LMs) demonstrate exceptional capabilities on tasks, but are vulnerable to extraction attacks. |
| Approach: | They propose Privacy Protection via Optimal Parameters (POP) which induces the model to forget about some of its training data. |
| Outcome: | The proposed method outperforms the state-of-the-art in retaining LM performance on 9 classification and 4 dialogue benchmarks. |
Prompting with Pseudo-Code Instructions (2023.emnlp-main)
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| Challenge: | ambiguity in natural language can hinder performance of large language models. |
| Approach: | They manually create a dataset of pseudo-code prompts for 132 different classification, QA, and generative language tasks, sourced from the Super-NaturalInstructions dataset. |
| Outcome: | The pseudo-code prompts improve the performance of two LLM families, BLOOM and CodeGen. |
LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation (2024.lrec-main)
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| Challenge: | Existing metrics for text summarisation have restrictive token limits, limiting their effectiveness. |
| Approach: | They propose a human-annotated data set for evaluating automatic factuality metrics . they propose 'longDocFACTScore' framework which can be extended to any length document . |
| Outcome: | The proposed framework outperforms state-of-the-art metrics in evaluating long document summarisation data sets. |
Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection (2024.findings-acl)
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| Challenge: | Existing models for implicit hate speech detection do not have significant advantage over cross-entropy loss-based learning. |
| Approach: | They propose a label-aware hard negative sampling strategy that encourages the model to learn detailed features from hard negative samples instead of random batch. |
| Outcome: | The proposed models outperform existing models for implicit hate speech detection both in- and cross-datasets. |
Emergent Inabilities? Inverse Scaling Over the Course of Pretraining (2023.findings-emnlp)
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| Challenge: | Recent research has found that increased number of model parameters and increased size of the training dataset positively influence model performance. |
| Approach: | They investigate whether language models' performance on specific tasks can decrease over the course of training. |
| Outcome: | The proposed model size-based scaling is found on 8 tasks on which Pythia 12B shows decreased performance over the course of training. |
Representation Projection Invariance Mitigates Representation Collapse (2023.findings-emnlp)
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| Challenge: | Fine-tuning contextualized representations by pre-trained models can lead to representation degradation, which can result in instability, sub-optimal performance, and weak generalization. |
| Approach: | They propose a regularization method to maintain the information content of representations and reduce representation collapse during fine-tuning. |
| Outcome: | The proposed method outperforms baselines on most tasks and improves out-of-distribution performance. |
Using Shapley interactions to understand how models use structure (2025.acl-long)
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| Challenge: | Language models are intricately structured systems, and attribution measures are important for understanding how they combine features to influence outputs. |
| Approach: | They use Shapley Taylor interaction indices to examine how language and speech models internally relate and structure their inputs. |
| Outcome: | The proposed methods show that language models encode phonetic interactions . they show that the inputs are more entangled for pairs where a consonant influences a vowel or approximant . |
Motivational Interviewing Transcripts Annotated with Global Scores (2024.lrec-main)
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| Challenge: | Motivational interviewing (MI) is a counseling approach that aims to increase intrinsic motivation and commitment to change. |
| Approach: | They propose to annotate MI therapy sessions written in English from public sources . they explore the potential use of the dataset for training MI language models . |
| Outcome: | The proposed dataset includes 242 MI demonstration transcripts annotated with therapist behavioral codes and global scores and client language EAsy Rating (CLEAR) tags for client speech. |
BERTwich: Extending BERT’s Capabilities to Model Dialectal and Noisy Text (2023.findings-emnlp)
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| Challenge: | Pre-trained language models like BERT deteriorate in the face of dialect variation or noise. |
| Approach: | They propose to sandwich BERT's encoder stack between additional encoder layers trained to perform masked language modeling on noisy text. |
| Outcome: | The proposed approach promotes zero-shot transfer to dialectal text and reduces embedding space between words and noisy counterparts. |
Comparative Analysis of the Intrinsic Metrics for Tokenizers and their effect on Downstream Tasks for Hindi and Marathi (2026.acl-long)
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| Challenge: | Various studies have shown that the performance of language models is poor in non-English or non-European languages. |
| Approach: | They propose a grapheme cluster tokenizer which shows better performance than other popular tokenizers. |
| Outcome: | The proposed tokenizers show better or competitiveness on question-answering tasks . the proposed tokenization model is highly correlated to the performance of other tokenizer models . |
Multimodal Language Models Show Evidence of Embodied Simulation (2024.lrec-main)
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| Challenge: | Multimodal large language models (MLLMs) are gaining popularity as partial solutions to the “symbol grounding problem” faced by language models trained on text alone. |
| Approach: | They propose to use multimodal large language models to integrate linguistic representations with data from other modalities to investigate whether they are integrated into a model. |
| Outcome: | The proposed models are sensitive to visual features like object shape when it is implied by a verbal description of an event. |
Efficient Unseen Language Adaptation for Multilingual Pre-Trained Language Models (2024.emnlp-main)
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| Challenge: | Multilingual pre-trained language models (mPLMs) have demonstrated notable effectiveness in zero-shot cross-lingual transfer tasks. |
| Approach: | They propose a method that uses soft-prompt tuning to tune for language adaptation . prompt tuning outperforms continuously trained baselines on two benchmarks . |
| Outcome: | The proposed approach outperforms baselines on two text classification benchmarks while utilizing 0.28% of tuned parameters. |
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation (2025.findings-acl)
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| Challenge: | incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. large language models are effective for generating explanatory rationales, but inconsistencies in relevance and domain alignment of LLM-generated rationale are challenging. |
| Approach: | They propose a framework that fine-tunes smaller language models with rationales exhibiting high domain relevance . they propose 'quality-focused' approach that selects rationale based on their alignment with clinical reasoning . |
| Outcome: | The proposed framework improves mental health detection and interpretation performance by ensuring high-quality rationales with domain relevance. |
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments (2025.acl-long)
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| Challenge: | Quantization has shown promise for Large Language Models, but current methods require lengthy training to alleviate quantization loss. |
| Approach: | They propose to decouple weights and incorporate Low-Rank adapters to reduce weight sharing . they validate the approach on LLaMA2 families and Mistral on downstream evaluation . |
| Outcome: | The proposed approach shows high performance while reducing deployment time faced with multiple scenarios. |
Towards Aligning Language Models with Textual Feedback (2024.emnlp-main)
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| Challenge: | Using textual feedback, language models can be trained to learn from textual inputs. |
| Approach: | They propose an approach that aligns language models with user preferences expressed in text. |
| Outcome: | The proposed approach outperforms PPO on toxicity reduction, summarization, and dialog response tasks while achieving the same performance with only 20% of the samples. |
Unveiling Multi-level and Multi-modal Semantic Representations in the Human Brain using Large Language Models (2024.emnlp-main)
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Yuko Nakagi, Takuya Matsuyama, Naoko Koide-Majima, Hiroto Yamaguchi, Rieko Kubo, Shinji Nishimoto, Yu Takagi
| Challenge: | Recent studies have assessed different levels of semantic content, such as speech, objects, and stories, separately. |
| Approach: | They used functional magnetic resonance imaging to record brain activity while watching 8.3 hours of dramas and movies. |
| Outcome: | The findings show that LLMs predict human brain activity more accurately than traditional language models, particularly for complex background stories. |
Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets (2025.emnlp-main)
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| Challenge: | Existing benchmarks for measuring gender stereotypical bias in language models are inconsistencies . lack of explicit standards in data gathering can have detrimental effects on results . |
| Approach: | They propose that currently available benchmarks capture only partial facets of gender stereotypes . they apply a framework from social psychology to balance data across components of gender stereotypes based on stereotypical benchmarks. |
| Outcome: | The proposed framework improves correlation between different benchmarks by using simple balancing techniques. |
BiasWipe: Mitigating Unintended Bias in Text Classifiers through Model Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods to mitigate unintended bias in social media platforms are re-training and adding extra parameters to the model. |
| Approach: | They propose a technique to mitigate unintended bias in language models by pruning the neuron weights responsible for univ bias. |
| Outcome: | The proposed technique achieves fairness by pruning the neuron weights responsible for unintended bias without loss of original performance. |
Exploiting Phonetics and Glyph Representation at Radical-level for Classical Chinese Understanding (2025.findings-acl)
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| Challenge: | Existing approaches in classical Chinese understanding have integrated heterogeneous linguistic knowledge, spanning lexicalsemantic relationships. |
| Approach: | They propose a radical-level phonetics and glyph representation enhanced Chinese model with powerful fine-grained semantic modeling capabilities. |
| Outcome: | The proposed model establishes robust representations through rules-based radical decomposition and bype pair encoder (BPE) based radical aggregated for structural pattern recognition, phonetic-glyph semantic mapping, and dynamic semantic fusion. |
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals. |
| Approach: | They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step. |
| Outcome: | The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks. |
Language Fusion for Parameter-Efficient Cross-lingual Transfer (2025.acl-long)
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| Challenge: | Limited availability of multilingual text corpora for pretraining results in poor performance on downstream tasks due to undertrained representation spaces for languages other than English. |
| Approach: | They propose a method that integrates source and target language representations within low-rank (LoRA) adapters using lightweight linear transformations to enhance representation quality and transfer performance for languages other than English. |
| Outcome: | The proposed method improves representation quality and performance for languages other than English while maintaining parameter efficiency. |
Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning (2025.acl-long)
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| Challenge: | Existing evaluations focus on final accuracy, neglecting the critical aspect of reasoning capabilities. |
| Approach: | They propose to evaluate LLMs’ abilities to detect and correct reasoning mistakes by using rule-based methods and smaller language models. |
| Outcome: | The proposed model outperforms existing models such as GPT-4o and GPT4 in both accuracy and accuracy, but lacks data contamination and memorization concerns. |
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to QA tables rely on independent row or column selection, fail to capture cross-row and cross-column dependencies, or attempt global reasoning. |
| Approach: | They propose a divide-and-conquer subtable selection framework that aggregates local evidence without requiring explicit global reasoning. |
| Outcome: | The proposed framework outperforms previous approaches to table QA in the noisy context. |
Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)
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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li
| Challenge: | Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly. |
| Approach: | They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show . |
| Outcome: | The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models. |
CPC-GRPO: Answer-Free Reinforcement Learning with Cross-Prompt Consensus Rewards (2026.findings-acl)
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| Challenge: | Reinforcement learning with verifiable rewards is a popular post-training tool for large language models, but relies on a ground-truth answer or external verifier, which limits applicability and increases cost. |
| Approach: | They propose an answer-free training objective that derives rewards solely from the model’s own probabilities by exploiting prompt paraphrases as multiple semantic views of the same intent. |
| Outcome: | The proposed objective derives rewards solely from the model’s own probabilities by exploiting prompt paraphrases as multiple semantic views of the same intent. |
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size (2026.findings-acl)
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Dikshant Kukreja, Kshitij Sah, Gautam Gupta, Avinash Anand, Rajiv Ratn Shah, Zhengkui Wang, Aik Beng Ng, Erik Cambria
| Challenge: | Larger language models become better and worse at handling contextual information . et al. (2017) formalized contextual entrainment as a tendency to favor tokens in context . |
| Approach: | They formalize the first scaling laws for contextual entrainment . they find large models are four times more resistant to counterfactual misinformation . |
| Outcome: | The largest models are four times more resistant to counterfactual misinformation than the smallest, but twice as prone to copying arbitrary tokens. |
Schoenfeld’s Anatomy of Mathematical Reasoning by Language Models (2026.acl-long)
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| Challenge: | Large language models expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statistics. |
| Approach: | They propose a framework that explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, Verify, etc. |
| Outcome: | The proposed framework reveals reproducible thinking dynamics and structural differences between reasoning and non-reasoning models, which are not apparent from token-level views. |
PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media (2026.acl-long)
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| Challenge: | Social media are shifting towards community-governed platforms where groups define their own norms. |
| Approach: | They propose a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities . they show that bigger models and increased context provide marginal gains, and universal rules like civility and self-promotion are easier to detect. |
| Outcome: | The proposed model can detect 13,371 rule violations across 1,989 Reddit communities across 2,885 rules in 9 languages. |
Exploring Large Language Models for Detecting Mental Disorders (2025.emnlp-main)
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| Challenge: | Detecting mental disorders and patient emotions through text analysis and machine learning is of increasing interest to researchers over the past decade. |
| Approach: | They compare the performance of traditional machine learning methods and encoder-based models on Russian-language datasets to those of large language models. |
| Outcome: | The proposed models outperform traditional methods on small and noisy datasets, but can perform comparable to language models when trained on patients with clinically confirmed depression. |
SycoBench-600: Measuring Sycophancy and Correction Selectivity in LLM Assistants (2026.findings-acl)
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| Challenge: | Existing evidence shows that instruction-following language models can be helpful and cooperative, but they also create reliability failure modes. |
| Approach: | They propose a benchmark that measures the ability to accept correct suggestions while resisting incorrect ones. |
| Outcome: | The proposed benchmark measures the ability to accept correct suggestions while resisting incorrect ones. |