---
title: "TACL 2024 — all accepted papers with summaries and citations"
description: "All 95 papers of TACL 2024, the most cited first; those with our summary link to it. Among them: A Closer Look at Classification Evaluation…"
canonical: "https://research.elamur.ai/free/en/venues/tacl/2024/papers/"
---
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# TACL 2024: all accepted papers

95 papers · the most cited first · [← TACL 2024](https://md.research.elamur.ai/free/en/venues/tacl/2024/)

1. **[A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice ↗](https://aclanthology.org/2024.tacl-1.46/)**Juri Opitz
2. **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/)**Xunyu Zhu, Jian Li, Yong Liu et al.*our summary*
3. **[ARN: Analogical Reasoning on Narratives ↗](https://aclanthology.org/2024.tacl-1.59/)**Zhivar Sourati, Filip Ilievski, Pia Sommerauer et al.
4. **[Addressing Topic Leakage in Cross-Topic Evaluation for Authorship Verification ↗](https://aclanthology.org/2024.tacl-1.75/)**Jitkapat Sawatphol, Can Udomcharoenchaikit, Sarana Nutanong
5. **[Addressing the Binning Problem in Calibration Assessment through Scalar Annotations ↗](https://aclanthology.org/2024.tacl-1.7/)**Zhengping Jiang, Anqi Liu, Benjamin Van Durme
6. **[AmbiFC: Fact-Checking Ambiguous Claims with Evidence ↗](https://aclanthology.org/2024.tacl-1.1/)**Max Glockner, Ieva Staliūnaitė, James Thorne et al.
7. **[An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation ↗](https://aclanthology.org/2024.tacl-1.8/)**Cheng Yang, Guoping Huang, Mo Yu et al.
8. **[Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation ↗](https://aclanthology.org/2024.tacl-1.22/)**Lukas Edman, Gabriele Sarti, Antonio Toral et al.
9. **[Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs ↗](https://aclanthology.org/2024.tacl-1.60/)**Harvey Lederman, Kyle Mahowald
10. **[Assessing the Role of Context in Chat Translation Evaluation: Is Context Helpful and Under What Conditions?](https://md.research.elamur.ai/free/en/papers/agrawal-2024-assessing-role-context-chat-translation/)**Sweta Agrawal, Amin Farajian, Patrick Fernandes et al.*our summary*
11. **[AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning ↗](https://aclanthology.org/2024.tacl-1.29/)**Han Zhou, Xingchen Wan, Ivan Vulić et al.
12. **[Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies](https://md.research.elamur.ai/free/en/papers/pan-2024-automatically-correcting-large-language-models/)**Liangming Pan, Michael Saxon, Wenda Xu et al.*our summary*
13. **[Benchmarking Large Language Models for News Summarization](https://md.research.elamur.ai/free/en/papers/zhang-2024-benchmarking-large-language-models-news/)**Tianyi Zhang, Faisal Ladhak, Esin Durmus et al.*our summary*
14. **[Beyond Boundaries: A Human-like Approach for Question Answering over Structured and Unstructured Information Sources ↗](https://aclanthology.org/2024.tacl-1.44/)**Jens Lehmann, Dhananjay Bhandiwad, Preetam Gattogi et al.
15. **[Beyond Prompt Brittleness: Evaluating the Reliability and Consistency of Political Worldviews in LLMs ↗](https://aclanthology.org/2024.tacl-1.76/)**Tanise Ceron, Neele Falk, Ana Barić et al.
16. **[Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts? ↗](https://aclanthology.org/2024.tacl-1.49/)**Cristina Aggazzotti, Nicholas Andrews, Elizabeth Allyn Smith
17. **[Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stability ↗](https://aclanthology.org/2024.tacl-1.74/)**Tyler A. Chang, Zhuowen Tu, Benjamin K. Bergen
18. **[Comparing Humans and Large Language Models on an Experimental Protocol Inventory for Theory of Mind Evaluation (EPITOME) ↗](https://aclanthology.org/2024.tacl-1.45/)**Cameron R. Jones, Sean Trott, Benjamin Bergen
19. **[Computational Complexity of Natural Morphology Revisited ↗](https://aclanthology.org/2024.tacl-1.36/)**Hajime Senuma, Akiko Aizawa
20. **[Conformal Prediction for Natural Language Processing: A Survey ↗](https://aclanthology.org/2024.tacl-1.82/)**Margarida Campos, António Farinhas, Chrysoula Zerva et al.
21. **[Conformalizing Machine Translation Evaluation ↗](https://aclanthology.org/2024.tacl-1.80/)**Chrysoula Zerva, André F. T. Martins
22. **[Context-Aware Machine Translation with Source Coreference Explanation ↗](https://aclanthology.org/2024.tacl-1.48/)**Huy Hien Vu, Hidetaka Kamigaito, Taro Watanabe
23. **[ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI ↗](https://aclanthology.org/2024.tacl-1.26/)**Sarah E. Finch, Jinho D. Choi
24. **[CreoleVal: Multilingual Multitask Benchmarks for Creoles ↗](https://aclanthology.org/2024.tacl-1.53/)**Heather Lent, Kushal Tatariya, Raj Dabre et al.
25. **[Cultural Adaptation of Recipes ↗](https://aclanthology.org/2024.tacl-1.5/)**Yong Cao, Yova Kementchedjhieva, Ruixiang Cui et al.
26. **[DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning ↗](https://aclanthology.org/2024.tacl-1.94/)**Jiaxin Guo, C. L. Philip Chen, Shuzhen Li et al.
27. **[Decision-Oriented Dialogue for Human-AI Collaboration ↗](https://aclanthology.org/2024.tacl-1.50/)**Jessy Lin, Nicholas Tomlin, Jacob Andreas et al.
28. **[Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design](https://md.research.elamur.ai/free/en/papers/tjuatja-2024-do-llms-exhibit-human-like/)**Lindia Tjuatja, Valerie Chen, Tongshuang Wu et al.*our summary*
29. **[Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation ↗](https://aclanthology.org/2024.tacl-1.62/)**Cyril Chhun, Fabian M. Suchanek, Chloé Clavel
30. **[Do Multi-Document Summarization Models Synthesize? ↗](https://aclanthology.org/2024.tacl-1.58/)**Jay DeYoung, Stephanie C. Martinez, Iain J. Marshall et al.
31. **[Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension ↗](https://aclanthology.org/2024.tacl-1.24/)**Sweta Agrawal, Marine Carpuat
32. **[Do Vision and Language Models Share Concepts? A Vector Space Alignment Study ↗](https://aclanthology.org/2024.tacl-1.68/)**Jiaang Li, Yova Kementchedjhieva, Constanza Fierro et al.
33. **[Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions](https://md.research.elamur.ai/free/en/papers/li-2024-eliciting-translation-ability-large-language/)**Jiahuan Li, Hao Zhou, Shujian Huang et al.*our summary*
34. **[Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering](https://md.research.elamur.ai/free/en/papers/adlakha-2024-evaluating-correctness-faithfulness-instruction-following/)**Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu et al.*our summary*
35. **[Evaluating the Ripple Effects of Knowledge Editing in Language Models ↗](https://aclanthology.org/2024.tacl-1.16/)**Roi Cohen, Eden Biran, Ori Yoran et al.
36. **[Explicitly Representing Syntax Improves Sentence-to-Layout Prediction of Unexpected Situations ↗](https://aclanthology.org/2024.tacl-1.15/)**Wolf Nuyts, Ruben Cartuyvels, Marie-Francine Moens
37. **[Exploring Continual Learning of Compositional Generalization in NLI ↗](https://aclanthology.org/2024.tacl-1.51/)**Xiyan Fu, Anette Frank
38. **[Exploring Human-Like Translation Strategy with Large Language Models](https://md.research.elamur.ai/free/en/papers/he-2024-exploring-human-like-translation-strategy/)**Zhiwei He, Tian Liang, Wenxiang Jiao et al.*our summary*
39. **[FINCH: Prompt-guided Key-Value Cache Compression for Large Language Models ↗](https://aclanthology.org/2024.tacl-1.83/)**Giulio Corallo, Paolo Papotti
40. **[Federated Learning for Exploiting Annotators' Disagreements in Natural Language Processing ↗](https://aclanthology.org/2024.tacl-1.35/)**Nuria Rodríguez-Barroso, Eugenio Martínez Cámara, Jose Camacho Collados et al.
41. **[Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence ↗](https://aclanthology.org/2024.tacl-1.87/)**Abhinav Patil, Jaap Jumelet, Yu Ying Chiu et al.
42. **[Geographic Adaptation of Pretrained Language Models ↗](https://aclanthology.org/2024.tacl-1.23/)**Valentin Hofmann, Goran Glavaš, Nikola Ljubešić et al.
43. **[Hierarchical Indexing for Retrieval-Augmented Opinion Summarization ↗](https://aclanthology.org/2024.tacl-1.84/)**Tom Hosking, Hao Tang, Mirella Lapata
44. **[Holmes \ensuremath\recorder A Benchmark to Assess the Linguistic Competence of Language Models ↗](https://aclanthology.org/2024.tacl-1.88/)**Andreas Waldis, Yotam Perlitz, Leshem Choshen et al.
45. **[How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://md.research.elamur.ai/free/en/papers/srikanth-2024-how-often-are-errors-natural/)**Neha Srikanth, Marine Carpuat, Rachel Rudinger*our summary*
46. **[Hypernetworks for Personalizing ASR to Atypical Speech ↗](https://aclanthology.org/2024.tacl-1.65/)**Max Müller-Eberstein, Dianna Yee, Karren Yang et al.
47. **[Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis ↗](https://aclanthology.org/2024.tacl-1.37/)**Sohee Yang, Jonghyeon Kim, Joel Jang et al.
48. **[IndoCulture: Exploring Geographically Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces ↗](https://aclanthology.org/2024.tacl-1.92/)**Fajri Koto, Rahmad Mahendra, Nurul Aisyah et al.
49. **[Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias ↗](https://aclanthology.org/2024.tacl-1.43/)**Itay Itzhak, Gabriel Stanovsky, Nir Rosenfeld et al.
50. **[Interactive Machine Teaching by Labeling Rules and Instances ↗](https://aclanthology.org/2024.tacl-1.79/)**Giannis Karamanolakis, Daniel Hsu, Luis Gravaano
51. **[Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization ↗](https://aclanthology.org/2024.tacl-1.64/)**George Chrysostomou, Zhixue Zhao, Miles Williams et al.
52. **[JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims ↗](https://aclanthology.org/2024.tacl-1.19/)**Fengzhu Zeng, Wei Gao
53. **[KoBBQ: Korean Bias Benchmark for Question Answering](https://md.research.elamur.ai/free/en/papers/jin-2024-kobbq-korean-bias-benchmark-question/)**Jiho Jin, Jiseon Kim, Nayeon Lee et al.*our summary*
54. **[L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models ↗](https://aclanthology.org/2024.tacl-1.72/)**Ansong Ni, Pengcheng Yin, Yilun Zhao et al.
55. **[Language Varieties of Italy: Technology Challenges and Opportunities ↗](https://aclanthology.org/2024.tacl-1.2/)**Alan Ramponi
56. **[Large Language Models Enable Few-Shot Clustering](https://md.research.elamur.ai/free/en/papers/viswanathan-2024-large-language-models-enable-few/)**Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence et al.*our summary*
57. **[Lost in the Middle: How Language Models Use Long Contexts ↗](https://aclanthology.org/2024.tacl-1.9/)**Nelson F. Liu, Kevin Lin, John Hewitt et al.
58. **[Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis ↗](https://aclanthology.org/2024.tacl-1.6/)**Shiman Zhao, Yutao Xie, Wei Chen et al.
59. **[Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion ↗](https://aclanthology.org/2024.tacl-1.57/)**Yongxue Shan, Jie Zhou, Jie Peng et al.
60. **[Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding ↗](https://aclanthology.org/2024.tacl-1.70/)**Ukyo Honda, Tatsushi Oka, Peinan Zhang et al.
61. **[NoviCode: Generating Programs from Natural Language Utterances by Novices ↗](https://aclanthology.org/2024.tacl-1.73/)**Asaf Achi Mordechai, Yoav Goldberg, Reut Tsarfaty
62. **[Predicting Human Translation Difficulty with Neural Machine Translation ↗](https://aclanthology.org/2024.tacl-1.81/)**Zheng Wei Lim, Ekaterina Vylomova, Charles Kemp et al.
63. **[Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers ↗](https://aclanthology.org/2024.tacl-1.71/)**Melanie Subbiah, Sean Zhang, Lydia B. Chilton et al. · 🏆 TACL 2024 Best Paper
64. **[Red Teaming Language Model Detectors with Language Models ↗](https://aclanthology.org/2024.tacl-1.10/)**Zhouxing Shi, Yihan Wang, Fan Yin et al.
65. **[Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization ↗](https://aclanthology.org/2024.tacl-1.86/)**Yangyang Zhao, Mehdi Dastani, Jinchuan Long et al.
66. **[Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval ↗](https://aclanthology.org/2024.tacl-1.66/)**Ohad Rubin, Jonathan Berant
67. **[Retrieval-style In-context Learning for Few-shot Hierarchical Text Classification ↗](https://aclanthology.org/2024.tacl-1.67/)**Huiyao Chen, Yu Zhao, Zulong Chen et al.
68. **[Retrieve What You Need: A Mutual Learning Framework for Open-domain Question Answering ↗](https://aclanthology.org/2024.tacl-1.14/)**Dingmin Wang, Qiuyuan Huang, Matthew Jackson et al.
69. **[Revisiting Meta-evaluation for Grammatical Error Correction ↗](https://aclanthology.org/2024.tacl-1.47/)**Masamune Kobayashi, Masato Mita, Mamoru Komachi
70. **[Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased? ↗](https://aclanthology.org/2024.tacl-1.95/)**Vagrant Gautam, Eileen Bingert, Dawei Zhu et al.
71. **[SCL: Selective Contrastive Learning for Data-driven Zero-shot Relation Extraction ↗](https://aclanthology.org/2024.tacl-1.93/)**Ning Pang, Xiang Zhao, Weixin Zeng et al.
72. **[SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks ↗](https://aclanthology.org/2024.tacl-1.55/)**Xuanli He, Qiongkai Xu, Jun Wang et al.
73. **[Scope Ambiguities in Large Language Models ↗](https://aclanthology.org/2024.tacl-1.41/)**Gaurav Kamath, Sebastian Schuster, Sowmya Vajjala et al.
74. **[Segmentation-Free Streaming Machine Translation ↗](https://aclanthology.org/2024.tacl-1.61/)**Javier Iranzo-Sánchez, Jorge Iranzo-Sánchez, Adrià Giménez et al.
75. **[Self-supervised Topic Taxonomy Discovery in the Box Embedding Space ↗](https://aclanthology.org/2024.tacl-1.77/)**Yuyin Lu, Hegang Chen, Pengbo Mao et al.
76. **[Semantics of Multiword Expressions in Transformer-Based Models: A Survey](https://md.research.elamur.ai/free/en/papers/miletic-2024-semantics-multiword-expressions-transformer-based/)**Filip Miletić, Sabine Schulte im Walde*our summary*
77. **[Simultaneous Selection and Adaptation of Source Data via Four-Level Optimization ↗](https://aclanthology.org/2024.tacl-1.25/)**Pengtao Xie, Xingchen Zhao, Xuehai He
78. **[Source-Free Domain Adaptation for Question Answering with Masked Self-training ↗](https://aclanthology.org/2024.tacl-1.40/)**Maxwell J. Yin, Boyu Wang, Yue Dong et al.
79. **[State of What Art? A Call for Multi-Prompt LLM Evaluation ↗](https://aclanthology.org/2024.tacl-1.52/)**Moran Mizrahi, Guy Kaplan, Dan Malkin et al.
80. **[TabVer: Tabular Fact Verification with Natural Logic ↗](https://aclanthology.org/2024.tacl-1.89/)**Rami Aly, Andreas Vlachos
81. **[Text Attribute Control via Closed-Loop Disentanglement ↗](https://aclanthology.org/2024.tacl-1.11/)**Lei Sha, Thomas Lukasiewicz
82. **[Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap ↗](https://aclanthology.org/2024.tacl-1.31/)**Michael Staniek, Raphael Schumann, Maike Züfle et al.
83. **[The Causal Influence of Grammatical Gender on Distributional Semantics ↗](https://aclanthology.org/2024.tacl-1.90/)**Karolina Stańczak, Kevin Du, Adina Williams et al.
84. **[The Ethics of Automating Legal Actors ↗](https://aclanthology.org/2024.tacl-1.39/)**Josef Valvoda, Alec Thompson, Ryan Cotterell et al.
85. **[The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations ↗](https://aclanthology.org/2024.tacl-1.17/)**Aina Garí Soler, Matthieu Labeau, Chloé Clavel
86. **[The Thai Discourse Treebank: Annotating and Classifying Thai Discourse Connectives](https://md.research.elamur.ai/free/en/papers/prasertsom-2024-thai-discourse-treebank-annotating-classifying/)**Ponrawee Prasertsom, Apiwat Jaroonpol, Attapol T. Rutherford*our summary*
87. **[To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation ↗](https://aclanthology.org/2024.tacl-1.20/)**Jiaming Luo, Colin Cherry, George Foster
88. **[Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models ↗](https://aclanthology.org/2024.tacl-1.91/)**Seong-Il Park, Jay-Yoon Lee
89. **[Unifying Structured Data as Graph for Data-to-Text Pre-Training ↗](https://aclanthology.org/2024.tacl-1.12/)**Shujie Li, Liang Li, Ruiying Geng et al.
90. **[Visually Grounded Speech Models Have a Mutual Exclusivity Bias ↗](https://aclanthology.org/2024.tacl-1.42/)**Leanne Nortje, Dan Oneaţă, Yevgen Matusevych et al.
91. **[What Do Self-Supervised Speech Models Know About Words? ↗](https://aclanthology.org/2024.tacl-1.21/)**Ankita Pasad, Chung-Ming Chien, Shane Settle et al.
92. **[What Formal Languages Can Transformers Express? A Survey ↗](https://aclanthology.org/2024.tacl-1.30/)**Lena Strobl, William Merrill, Gail Weiss et al.
93. **[When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/)**Ryo Kamoi, Yusen Zhang, Nan Zhang et al.*our summary*
94. **[mGPT: Few-Shot Learners Go Multilingual ↗](https://aclanthology.org/2024.tacl-1.4/)**Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova et al.
95. **[xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://md.research.elamur.ai/free/en/papers/guerreiro-2024-xcomet-transparent-machine-translation-evaluation/)**Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt et al.*our summary*
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