---
title: "CoNLL 2023 — all accepted papers with summaries and citations"
description: "All 72 papers of CoNLL 2023, the most cited first; those with our summary link to it. Among them: Med-HALT: Medical Domain Hallucination…"
canonical: "https://research.elamur.ai/free/en/venues/conll/2023/papers/"
---
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# CoNLL 2023: all accepted papers

72 papers · the most cited first · [← CoNLL 2023](https://md.research.elamur.ai/free/en/venues/conll/2023/)

1. **[Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://md.research.elamur.ai/free/en/papers/pal-2023-med-halt-medical-domain-hallucination/)**Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu*144 citations · our summary*
2. **[Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora ↗](https://aclanthology.org/2023.conll-babylm.1/)**Alex Warstadt, Aaron Mueller, Leshem Choshen et al.*69 citations*
3. **[Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests ↗](https://aclanthology.org/2023.conll-1.25/)**Max van Duijn, Bram van Dijk, Tom Kouwenhoven et al.*23 citations*
4. **[Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty ↗](https://aclanthology.org/2023.conll-babylm.24/)**Inar Timiryasov, Jean-Loup Tastet*21 citations*
5. **[Future Lens: Anticipating Subsequent Tokens from a Single Hidden State ↗](https://aclanthology.org/2023.conll-1.37/)**Koyena Pal, Jiuding Sun, Andrew Yuan et al.*9 citations*
6. **[Not all layers are equally as important: Every Layer Counts BERT ↗](https://aclanthology.org/2023.conll-babylm.20/)**Lucas Georges Gabriel Charpentier, David Samuel*8 citations*
7. **[Pre-training LLMs using human-like development data corpus ↗](https://aclanthology.org/2023.conll-babylm.30/)**Khushi Bhardwaj, Raj Sanjay Shah, Sashank Varma*7 citations*
8. **[Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization ↗](https://aclanthology.org/2023.conll-1.16/)**Ondrej Skopek, Rahul Aralikatte, Sian Gooding et al.*7 citations*
9. **[Exploring Transformers as Compact, Data-efficient Language Models ↗](https://aclanthology.org/2023.conll-1.35/)**Clayton Fields, Casey Kennington*6 citations*
10. **[Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics ↗](https://aclanthology.org/2023.conll-1.1/)**Yuhan Zhang, Edward Gibson, Forrest Davis*5 citations*
11. **[Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures ↗](https://aclanthology.org/2023.conll-babylm.12/)**Julius Steuer, Marius Mosbach, Dietrich Klakow*5 citations*
12. **[Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning ↗](https://aclanthology.org/2023.conll-1.20/)**Lucas Weber, Elia Bruni, Dieuwke Hupkes*5 citations*
13. **[Humans and language models diverge when predicting repeating text ↗](https://aclanthology.org/2023.conll-1.5/)**Aditya Vaidya, Javier Turek, Alexander Huth*4 citations*
14. **[ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind ↗](https://aclanthology.org/2023.conll-1.2/)**Xiaomeng Ma, Lingyu Gao, Qihui Xu*4 citations*
15. **[WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words ↗](https://aclanthology.org/2023.conll-babylm.21/)**Lukas Wolf, Klemen Kotar, Greta Tuckute et al.*4 citations*
16. **[Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings ↗](https://aclanthology.org/2023.conll-babylm.19/)**David Samuel*3 citations*
17. **[Tiny Language Models Enriched with Multimodal Knowledge from Multiplex Networks ↗](https://aclanthology.org/2023.conll-babylm.3/)**Clayton Fields, Osama Natouf, Andrew McMains et al.*3 citations*
18. **[ChiSCor: A Corpus of Freely-Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science ↗](https://aclanthology.org/2023.conll-1.23/)**Bram van Dijk, Max van Duijn, Suzan Verberne et al.*2 citations*
19. **[Cross-Document Event Coreference Resolution: Instruct Humans or Instruct GPT? ↗](https://aclanthology.org/2023.conll-1.38/)**Jin Zhao, Nianwen Xue, Bonan Min*2 citations*
20. **[Enhancing Code-mixed Text Generation Using Synthetic Data Filtering in Neural Machine Translation ↗](https://aclanthology.org/2023.conll-1.15/)**Dama Sravani, Radhika Mamidi*2 citations*
21. **[How Fragile is Relation Extraction under Entity Replacements? ↗](https://aclanthology.org/2023.conll-1.27/)**Yiwei Wang, Bryan Hooi, Fei Wang et al.*2 citations*
22. **[Investigating the Nature of Disagreements on Mid-Scale Ratings: A Case Study on the Abstractness-Concreteness Continuum ↗](https://aclanthology.org/2023.conll-1.6/)**Urban Knupleš, Diego Frassinelli, Sabine Schulte im Walde*2 citations*
23. **[JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models ↗](https://aclanthology.org/2023.conll-1.28/)**Yuiga Wada, Kanta Kaneda, Komei Sugiura*2 citations*
24. **[PSST! Prosodic Speech Segmentation with Transformers ↗](https://aclanthology.org/2023.conll-1.31/)**Nathan Roll, Calbert Graham, Simon Todd*2 citations*
25. **[REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization ↗](https://aclanthology.org/2023.conll-1.40/)**Mohammad Reza Ghasemi Madani, Pasquale Minervini*2 citations*
26. **[Strategies to Improve Low-Resource Agglutinative Languages Morphological Inflection ↗](https://aclanthology.org/2023.conll-1.34/)**Gulinigeer Abudouwaili, Wayit Ablez, Kahaerjiang Abiderexiti et al.*2 citations*
27. **[Syntactic Inductive Bias in Transformer Language Models: Especially Helpful for Low-Resource Languages? ↗](https://aclanthology.org/2023.conll-1.17/)**Luke Gessler, Nathan Schneider*2 citations*
28. **[The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks ↗](https://aclanthology.org/2023.conll-1.19/)**Kaiser Sun, Adina Williams, Dieuwke Hupkes*2 citations*
29. **[Towards more Human-like Language Models based on Contextualizer Pretraining Strategy](https://md.research.elamur.ai/free/en/papers/xiao-2023-more-human-like-language-models/)**Chenghao Xiao, G Thomas Hudson, Noura Al Moubayed*2 citations · our summary*
30. **[A surprisal oracle for active curriculum language modeling ↗](https://aclanthology.org/2023.conll-babylm.22/)**Xudong Hong, Sharid Loáiciga, Asad Sayeed*1 citation*
31. **[Acquiring Linguistic Knowledge from Multimodal Input](https://md.research.elamur.ai/free/en/papers/amariucai-2023-acquiring-linguistic-knowledge-multimodal-input/)**Theodor Amariucai, Alexander Scott Warstadt*1 citation · our summary*
32. **[Alignment via Mutual Information ↗](https://aclanthology.org/2023.conll-1.32/)**Shinjini Ghosh, Yoon Kim, Ramon Fernandez Astudillo et al.*1 citation*
33. **[ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages ↗](https://aclanthology.org/2023.conll-1.7/)**Mohammad Akbari, Saeed Ranjbar Alvar, Behnam Kamranian et al.*1 citation*
34. **[Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models ↗](https://aclanthology.org/2023.conll-babylm.13/)**Zheyu Zhang, Han Yang, Bolei Ma et al.*1 citation*
35. **[BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories? ↗](https://aclanthology.org/2023.conll-babylm.16/)**Xingmeng Zhao, Tongnian Wang, Sheri Osborn et al.*1 citation*
36. **[Better Together: Jointly Using Masked Latent Semantic Modeling and Masked Language Modeling for Sample Efficient Pre-training ↗](https://aclanthology.org/2023.conll-babylm.26/)**Gábor Berend*1 citation*
37. **[Byte-ranked Curriculum Learning for BabyLM Strict-small Shared Task 2023 ↗](https://aclanthology.org/2023.conll-babylm.17/)**Justin DeBenedetto*1 citation*
38. **[CLIMB – Curriculum Learning for Infant-inspired Model Building ↗](https://aclanthology.org/2023.conll-babylm.10/)**Richard Diehl Martinez, Zébulon Goriely, Hope McGovern et al.*1 citation*
39. **[Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior? ↗](https://aclanthology.org/2023.conll-babylm.9/)**Aryaman Chobey, Oliver Smith, Anzi Wang et al.*1 citation*
40. **[ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation ↗](https://aclanthology.org/2023.conll-babylm.6/)**Jaap Jumelet, Michael Hanna, Marianne de Heer Kloots et al.*1 citation*
41. **[CogMemLM: Human-Like Memory Mechanisms Improve Performance and Cognitive Plausibility of LLMs ↗](https://aclanthology.org/2023.conll-babylm.15/)**Lukas Thoma, Ivonne Weyers, Erion Çano et al.*1 citation*
42. **[GPT-wee: How Small Can a Small Language Model Really Get? ↗](https://aclanthology.org/2023.conll-babylm.2/)**Bastian Bunzeck, Sina Zarrieß*1 citation*
43. **[Grammar induction pretraining for language modeling in low resource contexts ↗](https://aclanthology.org/2023.conll-babylm.5/)**Xuanda Chen, Eva Portelance*1 citation*
44. **[HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities ↗](https://aclanthology.org/2023.conll-1.24/)**Esra Dönmez, Pascal Tilli, Hsiu-Yu Yang et al.*1 citation*
45. **[Implications of Annotation Artifacts in Edge Probing Test Datasets ↗](https://aclanthology.org/2023.conll-1.39/)**Sagnik Ray Choudhury, Jushaan Kalra*1 citation*
46. **[Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building ↗](https://aclanthology.org/2023.conll-babylm.29/)**Omar Momen, David Arps, Laura Kallmeyer*1 citation*
47. **[Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways ↗](https://aclanthology.org/2023.conll-babylm.27/)**Venkata S Govindarajan, Juan Diego Rodriguez, Kaj Bostrom et al.*1 citation*
48. **[Mini Minds: Exploring Bebeshka and Zlata Baby Models ↗](https://aclanthology.org/2023.conll-babylm.4/)**Irina Proskurina, Guillaume Metzler, Julien Velcin*1 citation*
49. **[Mmi01 at The BabyLM Challenge: Linguistically Motivated Curriculum Learning for Pretraining in Low-Resource Settings ↗](https://aclanthology.org/2023.conll-babylm.23/)**Maggie Mi*1 citation*
50. **[On the effect of curriculum learning with developmental data for grammar acquisition ↗](https://aclanthology.org/2023.conll-babylm.31/)**Mattia Opper, J. Morrison, N. Siddharth*1 citation*
51. **[Optimizing GPT-2 Pretraining on BabyLM Corpus with Difficulty-based Sentence Reordering ↗](https://aclanthology.org/2023.conll-babylm.32/)**Nasim Borazjanizadeh*1 citation*
52. **[Penn & BGU BabyBERTa+ for Strict-Small BabyLM Challenge ↗](https://aclanthology.org/2023.conll-babylm.7/)**Yahan Yang, Elior Sulem, Insup Lee et al.*1 citation*
53. **[Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization ↗](https://aclanthology.org/2023.conll-1.11/)**Gijs Wijnholds, Michael Moortgat*1 citation*
54. **[The Impact of Familiarity on Naming Variation: A Study on Object Naming in Mandarin Chinese ↗](https://aclanthology.org/2023.conll-1.30/)**Yunke He, Xixian Liao, Jialing Liang et al.*1 citation*
55. **[The Zipfian Challenge: Learning the statistical fingerprint of natural languages ↗](https://aclanthology.org/2023.conll-1.3/)**Christian Bentz*1 citation*
56. **[ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding ↗](https://aclanthology.org/2023.conll-babylm.14/)**Ömer Veysel Çağatan*1 citation*
57. **[Too Much Information: Keeping Training Simple for BabyLMs ↗](https://aclanthology.org/2023.conll-babylm.8/)**Lukas Edman, Lisa Bylinina*1 citation*
58. **[A Block Metropolis-Hastings Sampler for Controllable Energy-based Text Generation ↗](https://aclanthology.org/2023.conll-1.26/)**Jarad Forristal, Fatemehsadat Mireshghallah, Greg Durrett et al.
59. **[A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models ↗](https://aclanthology.org/2023.conll-1.8/)**Karin de Langis, Dongyeop Kang
60. **[A Minimal Approach for Natural Language Action Space in Text-based Games ↗](https://aclanthology.org/2023.conll-1.10/)**Dongwon Ryu, Meng Fang, Gholamreza Haffari et al.
61. **[Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue ↗](https://aclanthology.org/2023.conll-1.18/)**Aron Molnar, Jaap Jumelet, Mario Giulianelli et al.
62. **[BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisition](https://md.research.elamur.ai/free/en/papers/oba-2023-babylm-challenge-curriculum-learning-based/)**Miyu Oba, Akari Haga, Akiyo Fukatsu et al.*our summary*
63. **[Challenging the “One Single Vector per Token” Assumption ↗](https://aclanthology.org/2023.conll-1.33/)**Mathieu Dehouck
64. **[McGill BabyLM Shared Task Submission: The Effects of Data Formatting and Structural Biases ↗](https://aclanthology.org/2023.conll-babylm.18/)**Ziling Cheng, Rahul Aralikatte, Ian Porada et al.
65. **[MuLER: Detailed and Scalable Reference-based Evaluation ↗](https://aclanthology.org/2023.conll-1.29/)**Taelin Karidi, Leshem Choshen, Gal Patel et al.
66. **[On the Effects of Structural Modeling for Neural Semantic Parsing ↗](https://aclanthology.org/2023.conll-1.4/)**Xiang Zhang, Shizhu He, Kang Liu et al.
67. **[On the utility of enhancing BERT syntactic bias with Token Reordering Pretraining ↗](https://aclanthology.org/2023.conll-1.12/)**Yassir El Mesbahi, Atif Mahmud, Abbas Ghaddar et al.
68. **[PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments ↗](https://aclanthology.org/2023.conll-1.9/)**Daiki Asami, Saku Sugawara
69. **[Quantifying Information of Tokens for Simple and Flexible Simultaneous Machine Translation ↗](https://aclanthology.org/2023.conll-1.14/)**DongHyun Lee, Minkyung Park, Byung-Jun Lee
70. **[Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets ↗](https://aclanthology.org/2023.conll-1.13/)**Risako Owan, Maria Gini, Dongyeop Kang
71. **[Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing ↗](https://aclanthology.org/2023.conll-1.22/)**Brielen Madureira, Pelin Çelikkol, David Schlangen
72. **[Tree-shape Uncertainty for Analyzing the Inherent Branching Bias of Unsupervised Parsing Models ↗](https://aclanthology.org/2023.conll-1.36/)**Taiga Ishii, Yusuke Miyao
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