Mexico

Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of *Showerthoughts*, thoughts that may occur during mundane activities. We compare GPT-2 and GPT-Neo fine-tuned on Reddit data as well as GPT-3.5 invoked in a zero-shot manner, against human-authored texts. We measure human preference on the texts across the specific dimensions that account for the quality of creative, witty texts. Additionally, we compare the ability of humans versus fine-tuned RoBERTa-based classifiers to detect AI-generated texts. We conclude that human evaluators rate the generated texts slightly worse on average regarding their creative quality, but they are unable to reliably distinguish between human-written and AI-generated texts. We further provide the dataset for creative, witty text generation based on Reddit Showerthoughts posts.

NAACL 2024

Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit&#39;s Showerthoughts

showerthoughts

human evaluation

reddit

fine-tuning

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts

workshop paper

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Pursuing educational equity, particularly in writing instruction, requires that all students receive fair (i.e., accurate and unbiased) assessment and feedback on their texts. Automated Essay Scoring (AES) algorithms have so far focused on optimizing the mean accuracy of their scores and paid less attention to fair scores for all subgroups, although research shows that students receive unfair scores on their essays in relation to demographic variables, which in turn are related to their writing competence. We add to the literature arguing that AES should also optimize for fairness by presenting insights on the fairness of scoring algorithms on a corpus of learner texts in the German language and introduce the novelty of examining fairness on psychological and demographic differences in addition to demographic differences. We compare shallow learning, deep learning, and large language models with full and skewed subsets of training data to investigate what is needed for fair scoring. The results show that training on a skewed subset of higher and lower cognitive ability students shows no bias but very low accuracy for students outside the training set. Our results highlight the need for specific training data on all relevant user groups, not only for demographic background variables but also for cognitive abilities as psychological student characteristics.

Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education

Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the distractors, i.e., incorrect answers crafted to reflect student errors or misconceptions. Automatically generating them in math MCQs, e.g., with large language models, has been challenging. In this work, we propose a novel method to enhance the quality of generated distractors through overgenerate-and-rank, training a ranking model to predict how likely distractors are to be selected by real students. Experimental results on a real-world dataset and human evaluation with math teachers show that our ranking model increases alignment with human-authored distractors, although human-authored ones are still preferred over generated ones.

Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank

Natural language generation tools are powerful and effective for generating content. However, language models are known to display bias and fairness issues, making them impractical to deploy for many use cases. We here focus on how fairness issues impact automatically generated test content, which can have stringent requirements to ensure the test measures only what it was intended to measure. Specifically, we review test content generated for a large-scale standardized English proficiency test with the goal of identifying content that only pertains to a certain subset of the test population as well as content that has the potential to be upsetting or distracting to some test takers. Issues like these could inadvertently impact a test taker's score and thus should be avoided. This kind of content does not reflect the more commonly-acknowledged biases, making it challenging even for modern models that contain safeguards. We build a dataset of 601 generated texts annotated for fairness and explore a variety of methods for classification: fine-tuning, topic-based classification, and prompting, including few-shot and self-correcting prompts. We find that combining prompt self-correction and few-shot learning performs best, yielding an F1 score of 0.79 on our held-out test set, while much smaller BERT- and topic-based models have competitive performance on out-of-domain data.

Avoiding Taylor Swift: Identifying Fairness Issues in Automatically Generated Testing Content

Natural language processing (NLP) technology has rapidly improved automated grammatical error correction (GEC) tasks, and the GEC community has begun to explore document-level revision. However, there are two major obstacles to going beyond automated \textit{sentence-level} GEC to NLP-based document-level revision support: (1) there are few public corpora with document-level revisions annotated by professional editors, and (2) it is infeasible to obtain all possible references and evaluate revision quality using such references because there are infinite revision possibilities. To address these challenges, this paper proposes a new document revision corpus, Text Revision of ACL papers (TETRA), in which multiple professional editors have revised academic papers sampled from the ACL anthology. This corpus enables us to focus on document-level and paragraph-level edits, such as edits related to coherence and consistency. Additionally, as a case study using the TETRA corpus, we investigate reference-less and interpretable methods for meta-evaluation to detect quality improvements according to document revisions. We show the uniqueness of TETRA compared with existing document revision corpora and demonstrate that a fine-tuned pre-trained language model can discriminate the quality of documents after revision even when the difference is subtle.

Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond

The paper focuses on investigating vocabulary usage for AI and human-generated text. We define vocabulary usage in two ways: structural differences and keyword differences. Structural differences are evaluated by converting text into Vocabulary-Managment Profiles, initially used for discourse analysis. Through VMPs, we can treat the text data as a time series, allowing an evaluation by implementing Dynamic time-warping distance measures and subsequently deriving similarity scores to provide an indication of whether the structural dynamics in AI texts resemble human texts. To analyze keywords, we use a measure that emphasizes frequency and dispersion to source 'key' keywords. A qualitative approach is then applied, noting thematic differences between human and AI writing.

Evaluating Vocabulary Usage in LLMs

Individual feedback can help students improve their essay writing skills. However, the manual effort required to provide such feedback limits individualization in practice. Automatically-generated essay feedback may serve as an alternative to guide students at their own pace, convenience, and desired frequency. Large language models (LLMs) have demonstrated strong performance in generating coherent and contextually relevant text. Yet, their ability to provide helpful essay feedback is unclear. This work explores several prompting strategies for LLM-based zero-shot and few-shot generation of essay feedback. Inspired by Chain-of-Thought prompting, we study how and to what extent automated essay scoring (AES) can benefit the quality of generated feedback. We evaluate both the AES performance that LLMs can achieve with prompting only and the helpfulness of the generated essay feedback. Our results suggest that tackling AES and feedback generation jointly improves AES performance. However, while our manual evaluation emphasizes the quality of the generated essay feedback, the impact of essay scoring on the generated feedback remains low ultimately.

Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation

Teaching foreign languages and fostering language awareness in subject matter teaching requires a profound knowledge of grammar structures. Yet, while Large Language Models can act as tutors, it is unclear how effectively they can control grammar in generated text and adapt to learner needs. In this study, we investigate the ability of these models to exemplify pedagogically relevant grammar patterns, detect instances of grammar in a given text, and constrain text generation to grammar characteristic of a proficiency level. Concretely, we (1) evaluate the ability of GPT3.5 and GPT4 to generate example sentences for the standard English Grammar Profile CEFR taxonomy using few-shot in-context learning, (2) train BERT-based detectors with these generated examples of grammatical patterns, and (3) control the grammatical complexity of text generated by the open Mistral model by ranking sentence candidates with these detectors. We show that the grammar pattern instantiation quality is accurate but too homogeneous, and our classifiers successfully detect these patterns. A GPT-generated dataset of almost 1 million positive and negative examples for the English Grammar Profile is released with this work. With our method, Mistral's output significantly increases the number of characteristic grammar constructions on the desired level, outperforming GPT4. This showcases how language domain knowledge can enhance Large Language Models for specific education needs, facilitating their effective use for intelligent tutor development and AI-generated materials. 
Code, models, and data are available at https://github.com/dominikglandorf/LLM-grammar.

Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation

In this work, we investigate the potential of Large Language Models (LLMs) for automated short answer scoring. 
We test zero-shot and few-shot settings, and compare with fine-tuned models and a supervised upper-bound, across three diverse datasets. 
Our results, in zero-shot and few-shot settings, show that LLMs perform poorly in these settings: LLMs have difficulty with tasks that require complex reasoning or domain-specific knowledge. While the models show promise on general knowledge tasks. The fine-tuned model come close to the supervised results but are still not feasible for application, highlighting potential overfitting issues. 

Overall, our study highlights the challenges and limitations of LLMs in short answer scoring and indicates that there currently seems to be no basis for applying LLMs for short answer scoring.

LLMs in Short Answer Scoring: Limitations and Promise of Zero-Shot and Few-Shot Approaches

Massively multilingual machine translation models allow for the translation of a large number of languages with a single model, but have limited performance on low- and very-low-resource translation directions.
Pivoting via high-resource languages remains a strong strategy for low-resource directions, and in this paper we revisit ways of pivoting through multiple languages.
Previous work has used a simple averaging of probability distributions from multiple paths, but we find that this performs worse than using a single pivot, and exacerbates the hallucination problem because the same hallucinations can be probable across different paths. We also propose MaxEns, a novel combination strategy that makes the output biased towards the most confident predictions, hypothesising that confident predictions are less prone to be hallucinations.
We evaluate different strategies on the FLORES benchmark for 20 low-resource language directions, demonstrating that MaxEns improves translation quality for low-resource languages while reducing hallucination in translations, compared to both direct translation and an averaging approach. On average, multi-pivot strategies still lag behind using English as a single pivot language, raising the question of how to identify the best pivoting strategy for a given translation direction.

Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models

The interplay of cultural and linguistic elements that characterizes metaphorical language poses a substantial challenge for both human comprehension and machine processing. This challenge goes beyond monolingual settings and becomes particularly complex in translation, even more so in automatic translation. We present VOLIMET, a corpus of 2,916 parallel sentences containing gold standard alignments of metaphorical verb-object pairs and their literal paraphrases, e.g., tackle/address question, from English to German and French. On the one hand, the parallel nature of our corpus enables us to explore monolingual patterns for metaphorical vs. literal uses in English. On the other hand, we investigate different aspects of cross-lingual translations into German and French and the extent to which metaphoricity and literalness in the source language are transferred to the target languages. Monolingually, our findings reveal clear preferences in using metaphorical or literal uses of verb-object pairs. Cross-lingually, we observe a rich variability in translations as well as different behaviors for our two target languages.

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Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education

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