GenAI4ELab/papercli-papers huggingface dataset:
All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing
Authors: Advait Deshmukh; Ashwin Umadi; Dananjay Srinivas; Maria Leonor Pacheco
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we systematically demonstrate that entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, and that knowledge-infused approaches can account for some of these shortcomings. Our findings suggest that we need to go beyond PLMs to produce solutions that perform well for infrequent entities.
When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Authors: Junghyun Min; Xiulin Yang; Shira Wein
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Natural Language Inference (NLI) relies heavily on adequately parsing the semantic content of the premise and hypothesis.In this work, we investigate whether adding semantic information in the form of an Abstract Meaning Representation (AMR) helps pretrained language models better generalize in NLI. Our experiments integrating AMR into NLI in both fine-tuning and prompting settings show that the presence of AMR in fine-tuning hinders model generalization while prompting with AMR leads to slight gains in GPT-4o.However, an ablation study reveals that the improvement comes from amplifying surface-level differences rather than aiding semantic reasoning. This amplification can mislead models to predict non-entailment even when the core meaning is preserved.
Explanations explained. Influence of Free-text Explanations on LLMs and the Role of Implicit Knowledge
Authors: Andrea Zaninello; Roberto Dessi; Malvina Nissim; Bernardo Magnini
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: In this work, we investigate the relationship between the quality of explanations produced by different models and the amount of implicit knowledge the are able to provide beyond the input. We approximate explanation quality via accuracy on a downstream task with a standardized pipeline (GEISER) and study its correlation with three different association measures, each capturing different aspects of implicitness, defined as a combination of relevance and novelty. We conduct experiments with three SOTA LLMs on four tasks involving implicit knowledge, with explanations either confirming or contradicting the correct label. Our results demonstrate that providing quality explanations consistently improves the accuracy of LLM predictions, even when the models are not explicitly trained to take explanations as input, and underline the correlation between implicit content delivered by the explanation and its effectiveness.
Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning
Authors: Shambhavi Krishna; Atharva Naik; Chaitali Agarwal; Sudharshan Govindan; Haw-Shiuan Chang; Taesung Lee
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Large language models are increasingly deployed across diverse applications. This often includes tasks LLMs have not encountered during training.This implies that enumerating and obtaining the high-quality training data for all tasks is infeasible. Thus, we often need to rely on transfer learning using datasets with different characteristics, and anticipate out-of-distribution requests.Motivated by this practical need, we propose an analysis framework, building a transfer learning matrix and dimensionality reduction, to dissect these cross-task interactions.We train and analyze 10 models to identify latent abilities (e.g., Reasoning, Sentiment Classification, NLU, Arithmetic)and discover the side effects of the transfer learning.Our findings reveal that performance improvements often defy explanations based on surface-level dataset similarity or source data quality. Instead, hidden statistical factors of the source dataset, such as class distribution and generation length proclivities, alongside specific linguistic features, are actually more influential.This work offers insights into the complex dynamics of transfer learning, paving the way for more predictable and effective LLM adaptation.
LLMs as annotators of argumentation
Authors: Anna Lindahl
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Annotated data is essential for most NLP tasks, but creating it can be time-consuming and challenging. Argumentation annotation is especially complex, often resulting in moderate human agreement. While large language models (LLMs) have excelled in increasingly complex tasks, their application to argumentation annotation has been limited. This paper investigates how well GPT-4o and Claude can annotate three types of argumentation in Swedish data compared to human annotators. Using full annotation guidelines, we evaluate the models on argumentation schemes, argumentative spans, and attitude annotation. Both models perform similarly to humans across all tasks, with Claude showing better human agreement than GPT-4o. Agreement between models is higher than human agreement in argumentation scheme and span annotation.
If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition
Authors: Shubhashis Roy Dipta; Francis Ferraro
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as **3–6%**. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a **2–5%** improvement.
Modeling Language Learning in Corrective Feedback Interactions
Authors: Juan Luis Castro-Garcia; Parisa Kordjamshidi
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: To study computational models for language acquisition, we propose an interactive computational framework that utilizes a miniature language acquisition dataset in a controlled environment. In this framework, a neural learner model interacts with a teacher model that provides corrective feedback. Within this framework, we investigate various corrective feedback strategies, specifically focusing on reformulations and their effect on the learner model during their interactions. We design experimental settings to evaluate the learner’s production of syntactically and semantically correct linguistic utterances and perception of concepts and word-meaning associations.These results offer insights into the effectiveness of different feedback strategies in language acquisition using artificial neural networks. The outcome of this research is establishing a framework with a dataset for the systematic evaluation of various aspects of language acquisition in a controlled environment.
Relation-Aware Prompting Makes Large Language Models Effective Zero-shot Relation Extractors
Authors: Mahdi Rahimi; Razvan-Gabriel Dumitru; Mihai Surdeanu
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: While supervised relation extraction (RE) models have considerably advanced the state-of-the-art, they often perform poorly in low-resource settings. Zero-shot RE is vital when annotations are not available either due to costs or time constraints. As a result, zero-shot RE has garnered interest in the research community. With the advent of large language models (LLMs) many approaches have been proposed for prompting LLMs for RE, but these methods often either rely on an accompanying small language model (e.g., for finetuning on synthetic data generated by LLMs) or require complex post-prompt processing. In this paper, we propose an effective prompt-based method that does not require any additional resources. Instead, we use an LLM to perform a two-step process. In the first step, we perform a targeted summarization of the text with respect to the underlying relation, reduce the applicable label space, and synthesize examples. Then, we combine the products of these processes with other elements into a final prompt. We evaluate our approach with various LLMs on four real-world RE datasets. Our evaluation shows that our method outperforms the previous state-of-the-art zero-shot methods by a large margin. This work can also be considered as a new strong baseline for zero-shot RE that is compatible with any LLM.
Enhancing Readability-Controlled Text Modification with Readability Assessment and Target Span Prediction
Authors: Fengkai Liu; John S. Y. Lee
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Readability-controlled text modification aims to rewrite an input text so that it reaches a target level of difficulty. This task is closely related to automatic readability assessment (ARA) since, depending on the difficulty level of the input text, it may need to be simplified or complexified. Most previous research in LLM-based text modification has focused on zero-shot prompting, without further input from ARA or guidance on text spans that most likely require revision. This paper shows that ARA models for texts and sentences, as well as predictions of text spans that should be edited, can enhance performance in readability-controlled text modification.
TAG–EQA: Text–And–Graph for Event Question Answering via Structured Prompting Strategies
Authors: Maithili Sanjay Kadam; Francis Ferraro
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Large language models (LLMs) excel at general language tasks but often struggle with event-based questions—especially those requiring causal or temporal reasoning. We introduce TAG-EQA (Text-And-Graph for Event Question Answering), a prompting framework that injects causal event graphs into LLM inputs by converting structured relations into natural-language statements. TAG-EQA spans nine prompting configurations, combining three strategies (zero-shot, few-shot, chain-of-thought) with three input modalities (text-only, graph-only, text+graph), enabling a systematic analysis of when and how structured knowledge aids inference. On the TORQUESTRA benchmark, TAG-EQA improves accuracy by ~5% on average over text-only baselines, with gains up to ~12% in zero-shot settings and ~18% when graph-augmented CoT prompting is effective. While performance varies by model and configuration, our findings show that causal graphs can enhance event reasoning in LLMs without fine-tuning, offering a flexible way to encode structure in prompt-based QA.
DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding
Authors: Kin Ian Lo; Hala Hawashin; Mina Abbaszadeh; Tilen Gaetano Limbäck-Stokin; Hadi Wazni; Mehrnoosh Sadrzadeh
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Recent vision–language models excel at large-scale image–text alignment but often neglect the compositional structure of language, leading to failures on tasks that hinge on word order and predicate–argument structure. We introduce DisCoCLIP, a multimodal encoder that combines a frozen CLIP vision transformer with a novel tensor network text encoder that explicitly encodes syntactic structure. Sentences are parsed with a Combinatory Categorial Grammar parser to yield distributional word tensors whose contractions mirror the sentence’s grammatical derivation. To keep the model efficient, high-order tensors are factorized with tensor decompositions, reducing parameter count from tens of millions to under one million. Trained end-to-end with a self-supervised contrastive loss, DisCoCLIP markedly improves sensitivity to verb semantics and word order: it raises CLIP’s SVO-Probes verb accuracy from 77.6% to 82.4%, boosts ARO attribution and relation scores by over 9% and 4%, and achieves 93.7% on a newly introduced SVO-Swap benchmark. These results demonstrate that embedding explicit linguistic structure via tensor networks yields interpretable, parameter-efficient representations that substantially improve compositional reasoning in vision–language tasks.
HSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics
Authors: Dong Liu; Yanxuan Yu
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Semantic parsing of long documents remains challenging due to quadratic growth in pairwise composition and memory requirements. We introduce Hierarchical Segment-Graph Memory (HSGM), a novel framework that decomposes an input of length N into M meaningful segments, constructs Local Semantic Graphs on each segment, and extracts compact summary nodes to form a Global Graph Memory. HSGM supports incremental updates—only newly arrived segments incur local graph construction and summary-node integration—while Hierarchical Query Processing locates relevant segments via top-K retrieval over summary nodes and then performs fine-grained reasoning within their local graphs.Theoretically, HSGM reduces worst-case complexity from O(N2) to O(N\,k + (N/k)2),with segment size k ≪ N, and we derive Frobenius-norm bounds on the approximation error introduced by node summarization and sparsification thresholds. Empirically, on three benchmarks—long-document AMR parsing, segment-level semantic role labeling (OntoNotes), and legal event extraction—HSGM achieves 2–4× inference speedup, >60% reduction in peak memory, and ≥95% of baseline accuracy. Our approach unlocks scalable, accurate semantic modeling for ultra-long texts, enabling real-time and resource-constrained NLP applications.
Knowledge Editing Induces Underconfidence in Language Models
Authors: Ryo Hasegawa; Yusuke Sakai; Hidetaka Kamigaito; Taro Watanabe
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: As language models continue to scale, the demand for knowledge editing, a retraining-free knowledge update method, has increased. However, since knowledge editing directly alters token prediction probabilities acquired during pretraining, the probabilities may diverge from the empirical distribution. In this study, we analyze the impact of knowledge editing to compare the alignment between token prediction probabilities and task accuracy by calculating confidence calibration before and after knowledge editing. Our results reveal that, for tasks requiring semantic understanding, the range of increase in token prediction probabilities tends to be smaller than that of accuracy improvement, suggesting that knowledge editing methods lead to less confidence in prediction.
How Do Large Language Models Evaluate Lexical Complexity?
Authors: Abdelhak Kelious; Mathieu Constant; Christophe Coeur
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: In this work, we explore the prediction of lexical complexity by combining supervised approaches and the use of large language models (LLMs). We first evaluate the impact of different prompting strategies (zero-shot, one-shot, and chain-of-thought) on the quality of the predictions, comparing the results with human annotations from the CompLex 2.0 corpus. Our results indicate that LLMs, and in particular gpt-4o, benefit from explicit instructions to better approximate human judgments, although some discrepancies remain. Moreover, a calibration approach to better align LLMs predictions and human judgements based on few manually annotated data appears as a promising solution to improve the reliability of the annotations in a supervised scenario.
SAG: Enhancing Domain-Specific Information Retrieval with Semantic-Augmented Graphs
Authors: Carol-Luca Gasan; Vasile Pais
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Retrieval-Augmented Generation (RAG) systems rely on high-quality embeddings to retrieve relevant context for large language models. This paper introduces the Semantic-Augmented Graph (SAG), a new architecture that improves domain-specific embeddings by capturing hierarchical semantic relationships between text segments. Inspired by human information processing, SAG organizes content from general to specific concepts using a graph-based structure. By combining static embeddings with dynamic semantic graphs, it generates context-aware representations that reflect both lexical and conceptual links. Experiments on text similarity and domain-specific question answering show that SAG consistently outperforms standard embedding methods within RAG pipelines.
Cross-Domain Persuasion Detection with Argumentative Features
Authors: Bagyasree Sudharsan; Maria Leonor Pacheco
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: The main challenge in cross-domain persuasion detection lies in the vast differences in vocabulary observed across different outlets and contexts. Superficially, an argument made on social media will look nothing like an opinion presented in the Supreme Court, but the latent factors that make an argument persuasive are common across all settings. Regardless of domain, persuasive arguments tend to use sound reasoning and present solid evidence, build on the credibility and authority of the source, or appeal to the emotions and beliefs of the audience. In this paper, we show that simply encoding the different argumentative components and their semantic types can significantly improve a language model’s ability to detect persuasion across vastly different domains.
Hallucinated Span Detection with Multi-View Attention Features
Authors: Yuya Ogasa; Yuki Arase
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: This study addresses the problem of hallucinated span detection in the outputs of large language models. It has received less attention than output-level hallucination detection despite its practical importance. Prior work has shown that attentions often exhibit irregular patterns when hallucinations occur. Motivated by these findings, we extract features from the attention matrix that provide complementary views capturing (a) whether certain tokens are influential or ignored, (b) whether attention is biased toward specific subsets, and (c) whether a token is generated referring to a narrow or broad context, in the generation. These features are input to a Transformer-based classifier to conduct sequential labelling to identify hallucinated spans. Experimental results indicate that the proposed method outperforms strong baselines on hallucinated span detection with longer input contexts, such as data-to-text and summarisation tasks.
AdvERSEM: Adversarial Robustness Testing and Training of LLM-based Groundedness Evaluators via Semantic Structure Manipulation
Authors: Kaustubh Dhole; Ramraj Chandradevan; Eugene Agichtein
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: Evaluating outputs from large language models (LLMs) presents significant challenges, especially as hallucinations and adversarial manipulations are often difficult to detect. Existing evaluation methods lack robustness against subtle yet intentional linguistic alterations, necessitating novel techniques for reliably assessing model-generated content. Training accurate and robust groundedness evaluators is key for mitigating hallucinations and ensuring the alignment of model or human-generated claims to real-world evidence. However, as we show, many models, while optimizing for accuracy, lack robustness to subtle variations of claims, making them unsuitable and brittle in real-world settings where adversaries employ purposeful and deceitful tactics like hedging to deceive readers, which go beyond surface-level variations. To address this problem, we propose AdvERSem, a controllable adversarial approach to manipulating LLM output via Abstract Meaning Representations (AMR) to generate attack claims of multiple fine-grained types, followed by automatic verification of the correct label. By systematically manipulating a unique linguistic facet AdvERSem provides an interpretable testbed for gauging robustness as well as useful training data. We demonstrate that utilizing these AMR manipulations during training across multiple fact verification datasets helps improve the accuracy and robustness of groundedness evaluation while also minimizing the requirement of costly annotated data. To encourage further systematic evaluation, we release AdvERSem-Test, a manually verified groundedness test-bed.
Connecting Concept Layers and Rationales to Enhance Language Model Interpretability
Authors: Thomas Bailleux; Tanmoy Mukherjee; Pierre Marquis; Zied Bouraoui
Venue: EMNLP 2025 (Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025))

Abstract: With the introduction of large language models, NLP has undergone a paradigm shift where these models now serve as the backbone of most developed systems. However, while highly effective, they remain opaque and difficult to interpret, which limits their adoption in critical applications that require transparency and trust. Two major approaches aim to address this: rationale extraction, which highlights input spans that justify predictions, and concept bottleneck models, which make decisions through human-interpretable concepts. Yet each has limitations. Crucially, current models lack a unified framework that connects where a model looks (rationales) with why it makes a decision (concepts). We introduce CLARITY, a model that first selects key input spans, maps them to interpretable concepts, and then predicts using only those concepts. This design supports faithful, multi-level explanations and allows users to intervene at both the rationale and concept levels. CLARITY, achieves competitive accuracy while offering improved transparency and controllability.