---
title: "CoNLL 2024 — all accepted papers with summaries and citations"
description: "All 69 papers of CoNLL 2024, the most cited first; those with our summary link to it. Among them: Translating Across Cultures: LLMs for…"
canonical: "https://research.elamur.ai/free/en/venues/conll/2024/papers/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/venues/conll/2024/papers/)

[home](https://md.research.elamur.ai/free/en/) / [venues](https://md.research.elamur.ai/free/en/venues/) / [CoNLL](https://md.research.elamur.ai/free/en/venues/conll/) / [2024](https://md.research.elamur.ai/free/en/venues/conll/2024/) / papers

# CoNLL 2024: all accepted papers

69 papers · the most cited first · [← CoNLL 2024](https://md.research.elamur.ai/free/en/venues/conll/2024/)

1. **[Translating Across Cultures: LLMs for Intralingual Cultural Adaptation ↗](https://aclanthology.org/2024.conll-1.30/)**Pushpdeep Singh, Mayur Patidar, Lovekesh Vig*12 citations*
2. **[An Empirical Comparison of Vocabulary Expansion and Initialization Approaches For Language Models ↗](https://aclanthology.org/2024.conll-1.8/)**Nandini Mundra, Aditya Nanda Kishore Khandavally, Raj Dabre et al.*5 citations*
3. **[Global-Pruner: A Stable and Efficient Pruner for Retraining-Free Pruning of Encoder-Based Language Models ↗](https://aclanthology.org/2024.conll-1.5/)**Guangzhen Yao, Yuehan Wang, Hui Xu et al.*5 citations*
4. **[Revisiting Hierarchical Text Classification: Inference and Metrics ↗](https://aclanthology.org/2024.conll-1.18/)**Roman Plaud, Matthieu Labeau, Antoine Saillenfest et al.*5 citations*
5. **[Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection ↗](https://aclanthology.org/2024.conll-1.25/)**Soumyadeep Jana, Animesh Dey, Ranbir Singh Sanasam*4 citations*
6. **[Explaining the Hardest Errors of Contextual Embedding Based Classifiers ↗](https://aclanthology.org/2024.conll-1.31/)**Claudio Moisés Valiense De Andrade, Washington Cunha, Guilherme Fonseca et al.*4 citations*
7. **[Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy ↗](https://aclanthology.org/2024.conll-1.10/)**Pengfei Yu, Heng Ji*4 citations*
8. **[Lossy Context Surprisal Predicts Task-Dependent Patterns in Relative Clause Processing ↗](https://aclanthology.org/2024.conll-1.4/)**Kate McCurdy, Michael Hahn*4 citations*
9. **[Multi-Cultural Norm Base: Frame-based Norm Discovery in Multi-Cultural Settings ↗](https://aclanthology.org/2024.conll-1.3/)**Viet Thanh Pham, Shilin Qu, Farhad Moghimifar et al.*4 citations*
10. **[Critical Questions Generation: Motivation and Challenges ↗](https://aclanthology.org/2024.conll-1.9/)**Blanca Calvo Figueras, Rodrigo Agerri*3 citations*
11. **[Text2Afford: Probing Object Affordance Prediction abilities of Language Models solely from Text ↗](https://aclanthology.org/2024.conll-1.27/)**Sayantan Adak, Daivik Agrawal, Animesh Mukherjee et al.*3 citations*
12. **[The Effect of Surprisal on Reading Times in Information Seeking and Repeated Reading ↗](https://aclanthology.org/2024.conll-1.17/)**Keren Gruteke Klein, Yoav Meiri, Omer Shubi et al.*3 citations*
13. **[A Multimodal Large Language Model “Foresees” Objects Based on Verb Information but Not Gender ↗](https://aclanthology.org/2024.conll-1.32/)**Shuqi Wang, Xufeng Duan, Zhenguang Cai*2 citations*
14. **[Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network](https://md.research.elamur.ai/free/en/papers/fang-2024-advancing-arabic-sentiment-analysis-arsen/)**Yang Fang, Cheng Xu, Shuhao Guan et al.*2 citations · our summary*
15. **[CrowdCounter: A benchmark type-specific multi-target counterspeech dataset ↗](https://aclanthology.org/2024.conll-1.37/)**Punyajoy Saha, Abhilash Datta, Abhik Jana et al.*2 citations*
16. **[Generalizations across filler-gap dependencies in neural language models ↗](https://aclanthology.org/2024.conll-1.21/)**Katherine Howitt, Sathvik Nair, Allison Dods et al.*2 citations*
17. **[Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content ↗](https://aclanthology.org/2024.conll-1.40/)**Tyler Malloy, Maria José Ferreira, Fei Fang et al.*2 citations*
18. **[On Functional Competence of LLMs for Linguistic Disambiguation ↗](https://aclanthology.org/2024.conll-1.12/)**Raihan Kibria, Sheikh Intiser Uddin Dipta, Muhammad Abdullah Adnan*2 citations*
19. **[Words That Stick: Using Keyword Cohesion to Improve Text Segmentation ↗](https://aclanthology.org/2024.conll-1.1/)**Amit Maraj, Miguel Vargas Martin, Masoud Makrehchi*2 citations*
20. **[AIStorySimilarity: Quantifying Story Similarity Using Narrative for Search, IP Infringement, and Guided Creativity ↗](https://aclanthology.org/2024.conll-1.13/)**Jon Chun*1 citation*
21. **[EditEval: An Instruction-Based Benchmark for Text Improvements ↗](https://aclanthology.org/2024.conll-1.7/)**Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang et al.*1 citation*
22. **[Further Compressing Distilled Language Models via Frequency-aware Partial Sparse Coding of Embeddings ↗](https://aclanthology.org/2024.conll-1.29/)**Kohki Tamura, Naoki Yoshinaga, Masato Neishi*1 citation*
23. **[Is Structure Dependence Shaped for Efficient Communication?: A Case Study on Coordination ↗](https://aclanthology.org/2024.conll-1.23/)**Kohei Kajikawa, Yusuke Kubota, Yohei Oseki*1 citation*
24. **[Large Language Model Recall Uncertainty is Modulated by the Fan Effect ↗](https://aclanthology.org/2024.conll-1.24/)**Jesse Roberts, Kyle Moore, Douglas Fisher et al.*1 citation*
25. **[SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser ↗](https://aclanthology.org/2024.conll-1.14/)**Grusha Prasad, Tal Linzen*1 citation*
26. **[Self-supervised speech representations display some human-like cross-linguistic perceptual abilities ↗](https://aclanthology.org/2024.conll-1.35/)**Joselyn Rodriguez, Kamala Sreepada, Ruolan Leslie Famularo et al.*1 citation*
27. **[A Novel Instruction Tuning Method for Vietnamese Mathematical Reasoning using Trainable Open-Source Large Language Models ↗](https://aclanthology.org/2024.conll-1.20/)**Quang-Vinh Nguyen, Thanh-Do Nguyen, Van-Vinh Nguyen et al.
28. **[A surprisal oracle for when every layer counts ↗](https://aclanthology.org/2024.conll-babylm.21/)**Xudong Hong, Sharid Loáiciga, Asad Sayeed
29. **[Aligning Alignments: Do Colexification and Distributional Similarity Align as Measures of cross-lingual Lexical Alignment? ↗](https://aclanthology.org/2024.conll-1.26/)**Taelin Karidi, Eitan Grossman, Omri Abend
30. **[AntLM: Bridging Causal and Masked Language Models ↗](https://aclanthology.org/2024.conll-babylm.29/)**Xinru Yu, Bin Guo, Shiwei Luo et al.
31. **[Are BabyLMs Second Language Learners? ↗](https://aclanthology.org/2024.conll-babylm.14/)**Lukas Edman, Lisa Bylinina, Faeze Ghorbanpour et al.
32. **[Automatic Quality Estimation for Data Selection and Curriculum Learning ↗](https://aclanthology.org/2024.conll-babylm.18/)**Hiep Nguyen, Lynn Yip, Justin DeBenedetto
33. **[BERTtime Stories: Investigating the Role of Synthetic Story Data in Language Pre-training ↗](https://aclanthology.org/2024.conll-babylm.28/)**Nikitas Theodoropoulos, Giorgos Filandrianos, Vassilis Lyberatos et al.
34. **[BabyHGRN: Exploring RNNs for Sample-Efficient Language Modeling ↗](https://aclanthology.org/2024.conll-babylm.7/)**Patrick Haller, Jonas Golde, Alan Akbik
35. **[BabyLM Challenge: Experimenting with Self-Distillation and Reverse-Distillation for Language Model Pre-Training on Constrained Datasets ↗](https://aclanthology.org/2024.conll-babylm.3/)**Aakarsh Nair, Alina Hancharova, Mayank Kumar et al.
36. **[BabyLM Challenge: Exploring the effect of variation sets on language model training efficiency ↗](https://aclanthology.org/2024.conll-babylm.23/)**Akari Haga, Akiyo Fukatsu, Miyu Oba et al.
37. **[BabyLlama-2: Ensemble-Distilled Models Consistently Outperform Teachers With Limited Data ↗](https://aclanthology.org/2024.conll-babylm.26/)**Jean-Loup Tastet, Inar Timiryasov
38. **[Causal ATE Mitigates Unintended Bias in Controlled Text Generation ↗](https://aclanthology.org/2024.conll-1.11/)**Rahul Madhavan, Kahini Wadhawan
39. **[Choosy Babies Need One Coach: Inducing Mode-Seeking Behavior in BabyLlama with Reverse KL Divergence ↗](https://aclanthology.org/2024.conll-babylm.8/)**Shaozhen Shi, Yevgen Matusevych, Malvina Nissim
40. **[ConcreteGPT: A Baby GPT-2 Based on Lexical Concreteness and Curriculum Learning ↗](https://aclanthology.org/2024.conll-babylm.16/)**Luca Capone, Alessandro Bondielli, Alessandro Lenci
41. **[Developmentally Plausible Multimodal Language Models Are Highly Modular ↗](https://aclanthology.org/2024.conll-babylm.10/)**Alina Klerings, Christian Bartelt, Aaron Mueller
42. **[Different Ways to Forget: Linguistic Gates in Recurrent Neural Networks ↗](https://aclanthology.org/2024.conll-babylm.9/)**Cristiano Chesi, Veronica Bressan, Matilde Barbini et al.
43. **[Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data](https://md.research.elamur.ai/free/en/papers/alkhamissi-2024-dreaming-out-loud-self-synthesis/)**Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce et al.*our summary*
44. **[ELC-ParserBERT: Low-Resource Language Modeling Utilizing a Parser Network With ELC-BERT ↗](https://aclanthology.org/2024.conll-babylm.11/)**Rufus Behr
45. **[Exploring Curriculum Learning for Vision-Language Tasks: A Study on Small-Scale Multimodal Training ↗](https://aclanthology.org/2024.conll-babylm.6/)**Rohan Saha, Abrar Fahim, Alona Fyshe et al.
46. **[Extending the BabyLM Initiative : Promoting Diversity in Datasets and Metrics through High-Quality Linguistic Corpora ↗](https://aclanthology.org/2024.conll-babylm.12/)**Laurent Prévot, Sheng-Fu Wang, Jou-An Chi et al.
47. **[Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora ↗](https://aclanthology.org/2024.conll-babylm.1/)**Michael Y. Hu, Aaron Mueller, Candace Ross et al.
48. **[From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes ↗](https://aclanthology.org/2024.conll-babylm.4/)**Zébulon Goriely, Richard Diehl Martinez, Andrew Caines et al.
49. **[GPT or BERT: why not both? ↗](https://aclanthology.org/2024.conll-babylm.24/)**Lucas Georges Gabriel Charpentier, David Samuel
50. **[Global Learning with Triplet Relations in Abstractive Summarization ↗](https://aclanthology.org/2024.conll-1.15/)**Fengyu Lu, Jiaxin Duan, Junfei Liu
51. **[Graphemes vs. phonemes: battling it out in character-based language models ↗](https://aclanthology.org/2024.conll-babylm.5/)**Bastian Bunzeck, Daniel Duran, Leonie Schade et al.
52. **[How Are Metaphors Processed by Language Models? The Case of Analogies ↗](https://aclanthology.org/2024.conll-1.28/)**Joanne Boisson, Asahi Ushio, Hsuvas Borkakoty et al.
53. **[Image-conditioned human language comprehension and psychometric benchmarking of visual language models ↗](https://aclanthology.org/2024.conll-1.34/)**Subha Nawer Pushpita, Roger P. Levy
54. **[Integrating Quasi-symbolic Conceptual Knowledge into Language Model Pre-training ↗](https://aclanthology.org/2024.conll-babylm.13/)**Gábor Berend
55. **[Investigating large language models for their competence in extracting grammatically sound sentences from transcribed noisy utterances ↗](https://aclanthology.org/2024.conll-1.2/)**Alina Wróblewska
56. **[Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies ↗](https://aclanthology.org/2024.conll-babylm.15/)**Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely et al.
57. **[NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication ↗](https://aclanthology.org/2024.conll-1.19/)**Yuchen Lian, Tessa Verhoef, Arianna Bisazza
58. **[Of Models and Men: Probing Neural Networks for Agreement Attraction with Psycholinguistic Data ↗](https://aclanthology.org/2024.conll-1.22/)**Maxim Bazhukov, Ekaterina Voloshina, Sergey Pletenev et al.
59. **[One-Vs-Rest Neural Network English Grapheme Segmentation: A Linguistic Perspective ↗](https://aclanthology.org/2024.conll-1.36/)**Samuel Rose, Nina Dethlefs, C. Kambhampati
60. **[PRACT: Optimizing Principled Reasoning and Acting of LLM Agent ↗](https://aclanthology.org/2024.conll-1.33/)**Zhiwei Liu, Weiran Yao, Jianguo Zhang et al.
61. **[Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolution ↗](https://aclanthology.org/2024.conll-1.38/)**Ian Porada, Jackie CK Cheung
62. **[Teaching Tiny Minds: Exploring Methods to Enhance Knowledge Distillation for Small Language Models ↗](https://aclanthology.org/2024.conll-babylm.27/)**Hong Meng Yam, Nathan Paek
63. **[Towards Data-Efficient Language Models: A Child-Inspired Approach to Language Learning ↗](https://aclanthology.org/2024.conll-babylm.2/)**Mohammad Amin Ghanizadeh, Mohammad Javad Dousti
64. **[TpT-ADE: Transformer Based Two-Phase ADE Extraction ↗](https://aclanthology.org/2024.conll-1.16/)**Suryamukhi Kuchibhotla, Manish Singh
65. **[Transformer verbatim in-context retrieval across time and scale ↗](https://aclanthology.org/2024.conll-1.6/)**Kristijan Armeni, Marko Pranjić, Senja Pollak
66. **[Using Curriculum Masking Based on Child Language Development to Train a Large Language Model with Limited Training Data](https://md.research.elamur.ai/free/en/papers/lucas-2024-curriculum-masking-based-child-language/)**Evan Lucas, Dylan Gaines, Tagore Rao Kosireddy et al.*our summary*
67. **[What should Baby Models read? Exploring Sample-Efficient Data Composition on Model Performance ↗](https://aclanthology.org/2024.conll-babylm.25/)**Hong Meng Yam, Nathan Paek
68. **[WhatIf: Leveraging Word Vectors for Small-Scale Data Augmentation ↗](https://aclanthology.org/2024.conll-babylm.20/)**Alex Lyman, Bryce Hepner
69. **[When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets? ↗](https://aclanthology.org/2024.conll-babylm.17/)**Srikrishna Iyer
- [← back to](https://md.research.elamur.ai/free/en/venues/conll/2024/) — CoNLL 2024