Austria

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&#39; 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.

ACL 2025

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

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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.

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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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.

Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?

We present the ICLE-RC, a corpus of learner English texts annotated for relative clauses and related phenomena. The corpus contains a collection of 144 academic essays from the International Corpus of Learner English (ICLE; Granger et al., 2002), representing six L1 backgrounds – Finnish, Italian, Polish, Swedish, Turkish, and Urdu. These texts are annotated for over 900 relative clauses, with respect to a wide array of lexical, syntactic, semantic, and discourse features. The corpus also provides annotation of over 400 related phenomena (it-clefts, pseudo-clefts, existential-relatives, etc.). Here, we describe the corpus annotation framework, report on the IAA study, discuss the prospects of (semi-)automating annotation, and present the first results from our corpus analysis. We envisage the ICLE-RC to be used as a valuable resource for research on relative clauses in SLA, language typology, World Englishes, and discourse analysis.

ICLE-RC: International Corpus of Learner English for Relative Clauses

Wind energy project assessments present significant challenges for decision-makers, who must navigate and synthesize hundreds of pages of environmental and scientific documentation. These documents often span different regions and project scales, covering multiple domains of expertise. This process traditionally demands immense time and specialized knowledge from decision-makers. The advent of Large Language Models (LLM) and Retrieval Augmented Generation (RAG) approaches offer a transformative solution, enabling rapid, accurate cross-document information retrieval and synthesis. As the landscape of Natural Language Processing (NLP) and text generation continues to evolve, benchmarking becomes essential to evaluate and compare the performance of different RAG-based LLMs. In this paper, we present a comprehensive framework to generate a domain relevant RAG benchmark. Our framework is based on automatic question-answer generation with Human (domain experts)-AI (LLM) teaming. As a case study, we demonstrate the framework by introducing WeQA, a first-of-its-kind benchmark on the wind energy domain which comprises of multiple scientific documents/reports related to environmental aspects of wind energy projects. Our framework systematically evaluates RAG performance using diverse metrics and multiple question types with varying complexity level, providing a foundation for rigorous assessment of RAG-based systems in complex scientific domains and enabling researchers to identify areas for improvement in domain-specific applications.

Downloads

Next from ACL 2025

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

Similar lecture

DelucionQA: Detecting Hallucinations in Domain-specific Question Answering

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2025

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

Similar lecture

DelucionQA: Detecting Hallucinations in Domain-specific Question Answering

Downloads