Austria

We investigate cross-lingual transfer in large language models (LLMs) trained on two high-resource languages, English and Chinese. Four monolingual Chinese and four bilingual English-Chinese models are evaluated on two Chinese linguistic benchmarks. Monolingual models consistently outperform the bilingual ones on 12 out of 55 tasks, a result indicating negative transfer from English to Chinese. Additionally, we carry out a feature attribution analysis in a monolingual and a bilingual model, showing that the differences in their performance may be explained by more predictable attribution patterns in the monolingual model. Our findings have implications for the ongoing effort of training bilingual LLMs.

ACL 2025

Curse of bilinguality: Evaluating monolingual and bilingual language models on Chinese linguistic benchmarks

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

In-context learning is an effective method for large pre-trained language models (LLMs) to complete various downstream tasks. LLMs are typically trained in high-resource languages, and tasks in lower-resourced languages tend to underperform the higher-resource language counterparts for in-context learning. Despite the large body of work on prompting settings, it is still unclear how LLMs should be adapted cross-lingually specifically for in-context learning in the low-resource target languages. We perform a comprehensive study spanning five diverse target languages, three base LLMs, and seven downstream tasks spanning over 4,100 GPU training hours (9,900+ TFLOPs) across various adaptation techniques such as few-shot prompting, translate-test, fine-tuning, embedding re-initialization, and instruction fine-tuning. Our experiments display that few-shot prompting and translate-test settings tend to heavily outperform gradient-based adaptation methods, but there is no trend between which of the two works best. We also provide an empirical analysis of model outputs and demonstrate that the gradient-based adaptation methods faltered due to catastrophic forgetting. To the extent of our knowledge, this is the largest study done on in-context learning for low-resource languages with respect to train compute (time and FLOPs) and number of adaptation techniques considered. We make all our trained models available for public use.

Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages

The deployment of Large Language Models (LLMs) in customer support is constrained by hallucination—generating false information—and the high cost of proprietary models. To address these challenges, we propose a retrieval-augmented question-answering (QA) pipeline and explore how to balance human input and automation. Using a dataset of questions about a Samsung Smart TV user manual, we demonstrate that synthetic data generated by LLMs outperforms crowdsourced data in reducing hallucination in finetuned models. We also compare self-training (fine-tuning models on their own outputs) and knowledge distillation (fine-tuning on stronger models' outputs, e.g., GPT-4o), and find that self-training achieves comparable hallucination reduction. We conjecture that this surprising finding can be attributed to increased exposure bias issues in the knowledge distillation case and support this conjecture with post hoc analysis. We also improve robustness to unanswerable questions and retrieval failures with contextualized “I don’t know” responses. These findings show that scalable, cost-efficient QA systems can be built using synthetic data and self-training with open-source models, reducing reliance on proprietary tools or costly human annotations.

Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents

This study utilizes the game Codenames as a benchmarking tool to evaluate large language models (LLMs) with respect to specific linguistic and cognitive skills. LLMs play each side of the game, where one side generates a clue word covering several target words and the other guesses those target words. We designed various experiments by controlling the choice of words (abstract vs. concrete words, ambiguous vs. monosemic) or the opponent (programmed to be faster or slower in revealing words). Recent commercial and open-weight models were compared side-by-side to find out factors affecting their performance. The evaluation reveals details about their strategies, challenging cases, and limitations of LLMs.

Ad-hoc Concept Forming in the Game Codenames as a Means for Evaluating Large Language Models

We demonstrate that large multimodal language models differ substantially from humans in the distribution of coreferential expressions in a visual storytelling task. We introduce a number of metrics to quantify the characteristics of coreferential patterns in both human- and machine-written text. Humans distribute coreferential expressions in a way that maintains consistency across texts, interleaving references to different entities in a highly varied way. Machines are less able to track mixed references, despite achieving perceived improvements in generation quality.

Coreference as an indicator of context scope in multimodal narrative

Multiple-choice questions (MCQs) are often used to evaluate large language models (LLMs). They measure LLMs' general common sense and reasoning abilities, as well as their knowledge in specific domains such as law and medicine. However, the robustness of LLMs to various question formats in MCQs has not been thoroughly evaluated. While there are studies on the sensitivity of LLMs to input variations, research into their responsiveness to different question formats is still limited. Therefore, in this study, we propose a method to construct tasks to comprehensively evaluate the robustness against format changes of MCQs by decomposing the answering process into several steps. Using this dataset, we evaluate eight LLMs, such as Llama3-70B and Mixtral-8x7B. We find the lack of robustness to differences in the format of MCQs. It is crucial to consider whether the format of MCQs influences their evaluation scores when assessing LLMs using MCQ datasets.

MCQFormatBench: Robustness Tests for Multiple-Choice Questions

The ability of Large Language Models (LLMs) to follow natural language instructions is crucial. However, numerous studies have demonstrated that LLMs still struggle to follow instructions with complex constraints, limiting their application in other areas. Meanwhile, obtaining high-quality instruction-following data often requires substantial manual annotation, which is both time-consuming and labor-intensive. In this work, we present FiGV, a fine-grained constraint generation-verification strategy for synthesizing instruction-following data. FiGV employs LLM-driven processes to generate fine-grained constraints and check the legality of the synthetic instructions. Subsequently, LLMs are utilized to perform nuanced, constraint-level verification to determine whether the generated responses adhere to the synthetic instructions, with LLM-generated functions incorporated for auxiliary validation tailored to the types of constraints. Experiments on 7B to 70B models demonstrate that FiGV consistently achieves strong performance across various benchmarks designed to evaluate the instruction-following capabilities of LLMs.

Fine-Grained Constraint Generation-Verification for Improved Instruction-Following

Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensive tasks in finance remains difficult to assess due to major gaps in the methodologies of existing evaluation frameworks, which have caused an erroneous belief in a far lower bound of LMs' performance on common Finance NLP (FinNLP) tasks. To demonstrate the potential of LMs for these FinNLP tasks, we present the first holistic benchmarking suite for Financial Language Model Evaluation (FLaME). We are the first research paper to comprehensively study LMs against 'reasoning-reinforced' LMs, with an empirical study of 23 foundation LMs over 20 core NLP tasks in finance. We open-source our framework software along with all data and results.

Finance Language Model Evaluation (FLaME)

Keyphrase generation refers to the task of producing a set of words or phrases that summarises the content of a document. Continuous efforts have been dedicated to this task over the past few years, spreading across multiple lines of research, such as model architectures, data resources, and use-case scenarios. Yet, the current state of keyphrase generation remains unknown as there has been no attempt to review and analyse previous work. In this paper, we bridge this gap by presenting an analysis of over 50 research papers on keyphrase generation, offering a comprehensive overview of recent progress, limitations, and open challenges. Our findings highlight several critical issues in current evaluation practices, such as the concerning similarity among commonly-used benchmark datasets and inconsistencies in metric calculations leading to overestimated performances. Additionally, we address the limited availability of pre-trained models by releasing a strong PLM-based model for keyphrase generation as an effort to facilitate future research.

An Analysis of Datasets, Metrics and Models in Keyphrase Generation

Current evaluations of mathematical skills in Large Language Models (LLMs) is limited in scope for both textual and multi-modal problems, as existing benchmarks are either relatively small, focus on elementary to high school topics, or are based on Olympiad-style problems borrowed from high school contests, which, while challenging, represent a highly specific niche.

Additionally, while some benchmarks adhere to a multiple-choice answer format resulting in simpler tasks and faster saturation, others rely on LLM evaluators, resulting in potential biases and inconsistencies which often remain unaddressed.

To bridge these gaps, we introduce U-MATH, a novel benchmark comprised of 1,100 previously unpublished open-ended problems sourced from current curricula of multiple US universities. The dataset is balanced across six core subjects, with 20% of the problems incorporating visual elements.

Given the open-ended nature of U-MATH problems, we employ an LLM judge for solution assessment. To this end, we release mu-MATH --- a U-MATH-derived meta-evaluation benchmark, enabling rigorous assessment of LLM judges and their performance.

We evaluate a number of general-domain and math-specific LLMs, both proprietary and open-source. Our findings highlight limited performance of the current systems on university-level problems, with a maximum accuracy of 79.7% on textual tasks and an even lower score of 55% on multi-modal ones. Judging such problems proves to be non-trivial for LLMs, with the highest attained mu-MATH F1-score of 89.5%. Furthermore, with the higher-scoring models we observe a trade-off between problem-solving and judgment performance.

U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs

This paper introduces the new and challenging TempVS benchmark, which focuses on temporal grounding and reasoning capabilities of Multimodal Large Language Models (MLLMs) in image sequences. TempVS consists of three main tests (i.e., event relation inference, sentence ordering and image ordering) and each accompanied with a basic grounding test, yielding a total of 2,085 annotated image sequences and 15k multiple-choice questions. TempVS requires MLLMs to rely on both visual and linguistic modalities to understand the temporal order of events. We extensively evaluate 38 state-of-the-art MLLMs, demonstrating that models struggle to solve TempVS. Our analysis reveals a substantial performance gap between current MLLMs and human capabilities, accompanied by fine-grained insights that suggest promising directions for future research.

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