📰 CoNLL 2023 — accepted papers

CoNLL 2023 is the 2023 edition of the Conference on Computational Natural Language Learning (CoNLL), held in Singapore, December 2023 (publisher: Association for Computational Linguistics). The ACL Anthology lists 72 papers of it; our library holds 4 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

📍 Singapore · 🗓 December 2023

Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. Med-HALT: Medical Domain Hallucination Test for Large Language ModelsAnkit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu144 citations · our summary
2. Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora ↗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 ↗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 ↗Inar Timiryasov, Jean-Loup Tastet21 citations
5. Future Lens: Anticipating Subsequent Tokens from a Single Hidden State ↗Koyena Pal, Jiuding Sun, Andrew Yuan et al.9 citations
6. Not all layers are equally as important: Every Layer Counts BERT ↗Lucas Georges Gabriel Charpentier, David Samuel8 citations
7. Pre-training LLMs using human-like development data corpus ↗Khushi Bhardwaj, Raj Sanjay Shah, Sashank Varma7 citations
8. Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization ↗Ondrej Skopek, Rahul Aralikatte, Sian Gooding et al.7 citations
9. Exploring Transformers as Compact, Data-efficient Language Models ↗Clayton Fields, Casey Kennington6 citations
10. Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics ↗Yuhan Zhang, Edward Gibson, Forrest Davis5 citations

All accepted papers (72)

The most cited first; 4 with our summary link to their page here, the others to the original.

1. Med-HALT: Medical Domain Hallucination Test for Large Language ModelsAnkit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu144 citations · our summary
2. Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora ↗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 ↗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 ↗Inar Timiryasov, Jean-Loup Tastet21 citations
5. Future Lens: Anticipating Subsequent Tokens from a Single Hidden State ↗Koyena Pal, Jiuding Sun, Andrew Yuan et al.9 citations
6. Not all layers are equally as important: Every Layer Counts BERT ↗Lucas Georges Gabriel Charpentier, David Samuel8 citations
7. Pre-training LLMs using human-like development data corpus ↗Khushi Bhardwaj, Raj Sanjay Shah, Sashank Varma7 citations
8. Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization ↗Ondrej Skopek, Rahul Aralikatte, Sian Gooding et al.7 citations
9. Exploring Transformers as Compact, Data-efficient Language Models ↗Clayton Fields, Casey Kennington6 citations
10. Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics ↗Yuhan Zhang, Edward Gibson, Forrest Davis5 citations
11. Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures ↗Julius Steuer, Marius Mosbach, Dietrich Klakow5 citations
12. Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning ↗Lucas Weber, Elia Bruni, Dieuwke Hupkes5 citations
13. Humans and language models diverge when predicting repeating text ↗Aditya Vaidya, Javier Turek, Alexander Huth4 citations
14. ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind ↗Xiaomeng Ma, Lingyu Gao, Qihui Xu4 citations
15. WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words ↗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 ↗David Samuel3 citations
17. Tiny Language Models Enriched with Multimodal Knowledge from Multiplex Networks ↗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 ↗Bram van Dijk, Max van Duijn, Suzan Verberne et al.2 citations
19. Cross-Document Event Coreference Resolution: Instruct Humans or Instruct GPT? ↗Jin Zhao, Nianwen Xue, Bonan Min2 citations
20. Enhancing Code-mixed Text Generation Using Synthetic Data Filtering in Neural Machine Translation ↗Dama Sravani, Radhika Mamidi2 citations
21. How Fragile is Relation Extraction under Entity Replacements? ↗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 ↗Urban Knupleš, Diego Frassinelli, Sabine Schulte im Walde2 citations
23. JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models ↗Yuiga Wada, Kanta Kaneda, Komei Sugiura2 citations
24. PSST! Prosodic Speech Segmentation with Transformers ↗Nathan Roll, Calbert Graham, Simon Todd2 citations
25. REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization ↗Mohammad Reza Ghasemi Madani, Pasquale Minervini2 citations

See all 72 papers in CoNLL 2023 — accepted papers →

📄 Papers with our summary in our library (4)

The most cited first (OpenAlex citations plus our library's).

1. Med-HALT: Medical Domain Hallucination Test for Large Language ModelsConference on Computational Natural Language Learning (CoNLL)Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu2023 · 220 citations
2. BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisitionConference on Computational Natural Language Learning (CoNLL)Miyu Oba, Akari Haga, Akiyo Fukatsu, et al.2023 · 9 citations
3. Towards more Human-like Language Models based on Contextualizer Pretraining StrategyConference on Computational Natural Language Learning (CoNLL)Chenghao Xiao, G Thomas Hudson, Noura Al Moubayed2023 · 7 citations
4. Acquiring Linguistic Knowledge from Multimodal InputConference on Computational Natural Language Learning (CoNLL)Theodor Amariucai, Alexander Scott Warstadt2023 · 6 citations

🧭 Top topics

Topics of its works in our library, with the number of works on each.

🧠Language models & pretraining (1)🧒Psycholinguistics & language acquisition (1)🖼️Vision & language (1)

📚 Fields

🎭Arts and Humanities (4)🖥️Computer Science (3)🩺Medicine (1)

🧑‍🔬 Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

🏛️ Top institutions

Institutions on the bylines of its works here, by the number of works.

🎓Durham UniversityDurham · United Kingdom1 work here🎓ETH ZurichZurich · Switzerland1 work here🎓The University of TokyoTokyo · Japan1 work here🎓Nara Institute of Science and TechnologyIkoma · Japan1 work here

Place and dates from the event's site or the ACL Anthology; works and citations from OpenAlex and our library.

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