ymoslem/acl-abstracts huggingface dataset:
REVerSum: A Multi-staged Retrieval-Augmented Generation Method to Enhance Wikipedia Tail Biographies through Personal Narratives (2025)

Wikipedia is an invaluable resource for factual information about a wide range of entities. However, the quality of articles on less-known entities often lags behind that of the well-known ones. This study proposes a novel approach to enhancing Wikipedia`s B and C category biography articles by leveraging personal narratives such as autobiographies and biographies. By utilizing a multi-staged retrieval-augmented generation technique {--} REVerSum {--} we aim to enrich the informational content of these lesser-known articles. Our study reveals that personal narratives can significantly improve the quality of Wikipedia articles, providing a rich source of reliable information that has been underutilized in previous studies. Based on crowd-based evaluation, REVerSum generated content outperforms the best performing baseline by 17{\%} in terms of integrability to the original Wikipedia article and 28.5{\%} in terms of informativeness.
RE-FIN: Retrieval-based Enrichment for Financial data (2025)

Enriching sentences with knowledge from qualitative sources benefits various NLP tasks and enhances the use of labeled data in model training. This is crucial for Financial Sentiment Analysis (FSA), where texts are often brief and contain implied information. We introduce RE-FIN (Retrieval-based Enrichment for FINancial data), an automated system designed to retrieve information from a knowledge base to enrich financial sentences, making them more knowledge-dense and explicit. RE-FIN generates propositions from the knowledge base and employs Retrieval-Augmented Generation (RAG) to augment the original text with relevant information. A large language model (LLM) rewrites the original sentence, incorporating this data. Since the LLM does not create new content, the risk of hallucinations is significantly reduced. The LLM generates multiple new sentences using different relevant information from the knowledge base; we developed an algorithm to select one that best preserves the meaning of the original sentence while avoiding excessive syntactic similarity. Results show that enhanced sentences present lower perplexity than the original ones and improve performances on FSA.
SCV: Light and Effective Multi-Vector Retrieval with Sequence Compressive Vectors (2025)

Recent advances in language models (LMs) has driven progress in information retrieval (IR), effectively extracting semantically relevant information. However, they face challenges in balancing computational costs with deeper query-document interactions. To tackle this, we present two mechanisms: 1) a light and effective multi-vector retrieval with sequence compression vectors, dubbed SCV and 2) coarse-to-fine vector search. The strengths of SCV stems from its application of span compressive vectors for scoring. By employing a non-linear operation to examine every token in the document, we abstract these into a span-level representation. These vectors effectively reduce the document`s dimensional representation, enabling the model to engage comprehensively with tokens across the entire collection of documents, rather than the subset retrieved by Approximate Nearest Neighbor. Therefore, our framework performs a coarse single vector search during the inference stage and conducts a fine-grained multi-vector search end-to-end. This approach effectively reduces the cost required for search. We empirically show that SCV achieves the fastest latency compared to other state-of-the-art models and can obtain competitive performance on both in-domain and out-of-domain benchmark datasets.
Efficient Vocabulary Reduction for Small Language Models (2025)

The increasing size of large language models (LLMs) poses significant challenges due to their high computational costs and energy consumption, making their deployment in industrial settings difficult. Small language models (SLMs) have been introduced to mitigate these challenges by reducing model size while preserving performance. However, the embedding layer, which occupies a significant portion of the model, remains a bottleneck in model compression efforts. In this paper, we valdated vocabulary reduction as a solution to compress the embedding layer and reduce model size without significant loss of performance. We conduct a series of experiments to investigate how vocabulary reduction affects GPU memory footprint, inference speed, and task performance. Our results show that while performance generally declines with vocabulary reduction, fine-tuning can recover much of the lost performance. Moreover, in some tasks, such as truthfulness and summarization, the vocabulary-reduced models outperform the baseline. Finally, we demonstrate that vocabulary reduction can be effectively applied in domain adaptation, particularly in the medical domain, and in multilingual adaptation, improving task efficiency and cross-lingual robustness.
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025)

This paper studies the relevance modeling problem by integrating world knowledge stored in the parameters of LLMs with specialized domain knowledge represented by user behavior data for achieving promising performance. The novel framework ProRBP is proposed, which innovatively develops user-driven behavior neighbor retrieval module to learn domain-specific knowledge in time and introduces progressive prompting and aggregation module for considering diverse aspects of the relevance and prediction stability. We explore an industrial implementation to deploy LLMs to handle full-scale search traffics of Alipay with acceptable cost and latency. The comprehensive experiments on real-world industry data and online A/B testing validate the superiority of our proposal and the effectiveness of its main modules.
LLM ContextBridge: A Hybrid Approach for Intent and Dialogue Understanding in IVSR (2025)

In-vehicle speech recognition (IVSR) systems are crucial components of modern automotive interfaces, enabling hands-free control and enhancing user safety. However, traditional IVSR systems often struggle with interpreting user intent accurately due to limitations in contextual understanding and ambiguity resolution, leading to user frustration. This paper introduces LLM ContextBridge, a novel hybrid architecture that integrates Pretrained Language Model-based intent classification with Large Language Models to enhance both command recognition and dialogue management. LLM ContextBridge serves as a seamless bridge between traditional natural language understanding techniques and LLMs, combining the precise intent recognition of conventional NLU with the contextual handling and ambiguity resolution capabilities of LLMs. This approach significantly improves recognition accuracy and user experience, particularly in complex, multi-turn dialogues. Experimental results show notable improvements in task success rates and user satisfaction, demonstrating that LLM ContextBridge can make IVSR systems more intuitive, responsive, and context-aware.
Neural Document Segmentation Using Weighted Sliding Windows with Transformer Encoders (2025)

We introduce a novel Transformer-based method for document segmentation, tailored for practical, real-world applications. This method utilizes overlapping text sequences with a unique position-aware weighting mechanism to enhance segmentation accuracy. Through comprehensive experiments on both public and proprietary datasets, we demonstrate significant improvements, establishing new state-of-the-art standards by achieving up to a 10{\%} increase in segmentation F1 score compared to existing methods. Additionally, we explore the application of our segmentation method in downstream retrieval-augmented question answering tasks, where it improves the quality of generated responses by 5{\%} while achieving up to four times greater efficiency. These results underscore our model`s potential as a robust and scalable solution for real-world text segmentation challenges.
RecStream: Graph-aware Stream Management for Concurrent Recommendation Model Online Serving (2025)

Recommendation Models (RMs) are crucial for predicting user preferences and enhancing personalized experiences on large-scale platforms. As the application of recommendation models grows, optimizing their online serving performance has become a significant challenge. However, current serving systems perform poorly under highly concurrent scenarios. To address this, we introduce RecStream, a system designed to optimize stream configurations based on model characteristics for handling high concurrency requests. We employ a hybrid Graph Neural Network architecture to determine the best configurations for various RMs. Experimental results demonstrate that RecStream achieves significant performance improvements, reducing latency by up to 74{\%}.
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models (2025)

Large Language Models (LLMs) exhibit impressive performance across various domains but still struggle with arithmetic reasoning tasks. Recent work shows the effectiveness of prompt design methods in enhancing reasoning capabilities. However, these approaches overlook crucial requirements for prior knowledge of specific concepts, theorems, and tricks to tackle most arithmetic reasoning problems successfully. To address this issue, we propose a novel and effective Teaching-Inspired Integrated Prompting Framework, which emulates the instructional process of a teacher guiding students. This method equips LLMs with essential concepts, relevant theorems, and similar problems with analogous solution approaches, facilitating the enhancement of reasoning abilities. Additionally, we introduce two new Chinese datasets, MathMC and MathToF, both with detailed explanations and answers. Experiments are conducted on nine benchmarks which demonstrates that our approach improves the reasoning accuracy of LLMs. With GPT-4 and our framework, we achieve new state-of-the-art performance on four math benchmarks (AddSub, SVAMP, Math23K and AQuA) with accuracies of 98.2{\%} (+3.3{\%}), 93.9{\%} (+0.2{\%}), 94.3{\%} (+7.2{\%}) and 81.1{\%} (+1.2{\%}).
Linguistic Blind Spots of Large Language Models (2025)

Large language models (LLMs) serve as the foundation of numerous AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability in performing fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses or T-units in input texts. These tasks require precise syntactic and semantic understanding of input text, and when LLMs underperform on specific linguistic structures, it raises concerns about their reliability for detailed linguistic analysis and whether their (even correct) outputs truly reflect an understanding of the inputs. In this paper, we empirically study recent LLMs performance across fine-grained linguistic annotation tasks. Through a series of experiments, we find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. We show that the most capable LLM (Llama3-70b) makes notable errors in detecting linguistic structures, such as misidentifying embedded clauses, failing to recognize verb phrases, and confusing complex nominals with clauses. Our study provides valuable insights to inform future endeavors in LLM design and development.
ParaBLoCC: Parallel Basic Locative Constructions Corpus (2025)

We introduce ParaBLoCC, the Parallel Basic Locative Construction Corpus, the first multilingual compendium of this important grammatico-functional construction, and particularly the first such corpus containing semantically equivalent BLCs in source/target language pairs. The data {--} taken from bitext corpora in English paired with twenty-six typologically diverse languages {--} are likely to prove useful for studying questions of cognitive underpinnings and cross-linguistic usage patterns of spatial expressions, as well as for improving multilingual spatial relation extraction and related tasks. The data are being made available at https://github.com/pviechnicki/parablocc.
Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser (2025)

A parser for Minimalist grammars (Stabler, 2013) has been shown to successfully model sentence processing preferences across an array of languages and phenomena when combined with complexity metrics that relate parsing behavior to memory usage (Gerth, 2015; Graf et al., 2017; De Santo, 2020, a.o.). This model provides a quantifiable theory of the effects of fine-grained grammatical structure on cognitive cost, and can help strengthen the link between generative syntactic theory and sentence processing.However, work on it has focused on offline asymmetries.Here, we extend this approach by showing how memory-based measures of effort that explicitly consider minimalist-like structure-building operations improve our ability to account for word-by-word (online) behavioral data.
From Punchlines to Predictions: A Metric to Assess LLM Performance in Identifying Humor in Stand-Up Comedy (2025)

Comedy serves as a profound reflection of the times we live in and is a staple element of human interactions. In light of the widespread adoption of Large Language Models (LLMs), the intersection of humor and AI has become no laughing matter. Advancements in the naturalness of human-computer interaction correlates with improvements in AI systems' abilities to understand humor. In this study, we assess the ability of models in accurately identifying humorous quotes from a stand-up comedy transcript. Stand-up comedy`s unique comedic narratives make it an ideal dataset to improve the overall naturalness of comedic understanding. We propose a novel humor detection metric designed to evaluate LLMs amongst various prompts on their capability to extract humorous punchlines. The metric has a modular structure that offers three different scoring methods - fuzzy string matching, sentence embedding, and subspace similarity - to provide an overarching assessment of a model`s performance. The model`s results are compared against those of human evaluators on the same task. Our metric reveals that regardless of prompt engineering, leading models, ChatGPT, Claude, and DeepSeek, achieve scores of at most 51{\%} in humor detection. Notably, this performance surpasses that of humans who achieve a score of 41{\%}. The analysis of human evaluators and LLMs reveals variability in agreement, highlighting the subjectivity inherent in humor and the complexities involved in extracting humorous quotes from live performance transcripts.
Profiling neural grammar induction on morphemically tokenised child-directed speech (2025)

We investigate the performance of state-of-the-art (SotA) neural grammar induction (GI) models on a morphemically tokenised English dataset based on the CHILDES treebank (Pearl and Sprouse, 2013). Using implementations from Yang et al. (2021a), we train models and evaluate them with the standard F1 score. We introduce novel evaluation metrics{---}depth-of-morpheme and sibling-of-morpheme{---}which measure phenomena around bound morpheme attachment. Our results reveal that models with the highest F1 scores do not necessarily induce linguistically plausible structures for bound morpheme attachment, highlighting a key challenge for cognitively plausible GI.
Exploring the Integration of Eye Movement Data on Word Embeddings (2025)

Reading, while structured, is a non-linear process. Readers may skip some words, linger on others, or revisit earlier text. Emerging work has started exploring the incorporation of reading behaviour through eye-tracking into the training of specific language tasks. In this work, we investigate the broader question of how gaze data can shape word embeddings by using text as read by human participants and predicting gaze measures from them. To that end, we conducted an eye-tracking experiment with 76 participants reading 20 short stories in Spanish and fine-tuned Word2Vec and LSTM models on the collected data. Evaluations with representational similarity analysis and word pair similarities showed a limited, but largely consistent, gain from gaze incorporation, suggesting future work should expand linguistic diversity and use cognitively aligned evaluations to better understand its role in bridging computational and human language representations.
Unzipping the Causality of Zipf`s Law and Other Lexical Trade-offs (2025)

There are strong constraints on the structure of a possible lexicon. For example, the negative correlation between word frequency and length known as Zipf`s law, and a negative correlation between word length and phonotactic complexity appear to hold across languages. While lexical trade-offs like these have been examined individually, it is unclear how they interact as a system. In this paper, we propose causal discovery as a method for identifying lexical biases and their interactions in a set of variables. We represent the lexicon as a causal model, and apply the Fast Causal Discovery algorithm (Spirtes et al., 1995) to identify both causal relationships between measured variables and the existence of possible unmeasured confounding variables. We apply this method to lexical data including measures of word length, frequency, phonotactic complexity, and morphological irregularity for 25 languages and find evidence of universal associations involving word length with a high likelihood of involving an unmeasured confounder, suggesting that additional variables need to be measured to determine how they are related. We also find evidence of variation across languages in relationships between the remaining variables, and suggest that given a larger dataset, causal discovery algorithms can be a useful tool in assessing the universality of lexical biases.
Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language (2025)

Although functional specialization in the brain - a phenomenon where different regions process different types of information - is well documented, we still lack precise mathematical methods with which to measure it. This work proposes a technique to quantify how brain regions respond to distinct categories of information. Using a topic encoding model, we identify brain regions that respond strongly to specific semantic categories while responding minimally to all others. We then use a language model to characterize the common themes across each region`s preferred categories. Our technique successfully identifies previously known functionally selective regions and reveals consistent patterns across subjects while also highlighting new areas of high specialization worthy of further study.
\textquotedblleftIs There Anything Else?\textquotedblright: Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test (2025)

Alzheimer`s Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in naturalistic language samples can be useful for early screening of AD dementia. However, the nature of language deficits often requires test administrators to use various speech elicitation techniques during spontaneous language assessments to obtain enough propositional utterances from dementia patients. This could lead to the {\textquotedblleft}observer`s effect{\textquotedblright} on the downstream analysis that has not been fully investigated. Our study seeks to quantify the influence of test administrators on linguistic features in dementia assessment with two English corpora the {\textquotedblleft}Cookie Theft{\textquotedblright} picture description datasets collected at different locations and test administrators show different levels of administrator involvement. Our results show that the level of test administrator involvement significantly impacts observed linguistic features in patient speech. These results suggest that many of significant linguistic features in the downstream classification task may be partially attributable to differences in the test administration practices rather than solely to participants' cognitive status. The variations in test administrator behavior can lead to systematic biases in linguistic data, potentially confounding research outcomes and clinical assessments. Our study suggests that there is a need for a more standardized test administration protocol in the development of responsible clinical speech analytics frameworks.
Cross-Framework Generalizable Discourse Relation Classification Through Cognitive Dimensions (2025)

Existing discourse corpora annotated under different frameworks adopt distinct but somewhat related taxonomies of relations. How to integrate discourse frameworks has been an open research question. Previous studies on this topic are mainly theoretical, although such research is typically performed with the hope of benefiting computational applications. In this paper, we show how the proposal by Sanders et al. (2018) based on the Cognitive approach to Coherence Relations (CCR) (Sanders et al.,1992, 1993) can be used effectively to facilitate cross-framework discourse relation (DR) classification. To address the challenges of using predicted UDims for DR classification, we adopt the Bayesian learning framework based on Monte Carlo dropout (Gal and Ghahramani, 2016) to obtain more robust predictions. Data augmentation enabled by our proposed method yields strong performance (55.75 for RST and 55.01 for PDTB implicit DR classification in macro-averaged F1). We compare four model designs and analyze the experimental results from different perspectives. Our study shows an effective and cross-framework generalizable approach for DR classification, filling a gap in existing studies.
Distinct social-linguistic processing between humans and large audio-language models: Evidence from model-brain alignment (2025)

Voice-based AI development faces unique challenges in processing both linguistic and paralinguistic information. This study compares how large audio-language models (LALMs) and humans integrate speaker characteristics during speech comprehension, asking whether LALMs process speaker-contextualized language in ways that parallel human cognitive mechanisms. We compared two LALMs' (Qwen2-Audio and Ultravox 0.5) processing patterns with human EEG responses. Using surprisal and entropy metrics from the models, we analyzed their sensitivity to speaker-content incongruency across social stereotype violations (e.g., a man claiming to regularly get manicures) and biological knowledge violations (e.g., a man claiming to be pregnant). Results revealed that Qwen2-Audio exhibited increased surprisal for speaker-incongruent content and its surprisal values significantly predicted human N400 responses, while Ultravox 0.5 showed limited sensitivity to speaker characteristics. Importantly, neither model replicated the human-like processing distinction between social violations (eliciting N400 effects) and biological violations (eliciting P600 effects). These findings reveal both the potential and limitations of current LALMs in processing speaker-contextualized language, and suggest differences in social-linguistic processing mechanisms between humans and LALMs.