India

Fine-tuning Large Language Models(LLMs) on multi-turn reasoning datasets requires N (number of turns) separate forward passes per conversation due to reasoning token visibility constraints, as reasoning tokens for a turn are discarded in subsequent turns. We propose duplicating response tokens along with a custom attention mask to enable single-pass processing of entire conversations. We prove our method produces identical losses to the N-pass approach while reducing time complexity from $O\bigl(N^{3}\bigl)$ to $O\bigl(N^{2}\bigl)$ and maintaining the same memory complexity for a transformer based model. Our approach achieves significant training speedup while preserving accuracy. Our implementation is available online(https://github.com/devrev/One-Pass-to-Reason).

IJCNLP-AACL 2025

One-Pass to Reason: Token Duplication and Block-Sparse Mask for Efficient Fine-Tuning on Multi-Turn Reasoning

multi-turn training

reasoning in language models

optimisation

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

poster

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Knowledge distillation (KD) is a popular method of transferring knowledge from a large "teacher" model to a small "student" model.  Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for intermediate-layer matching in KD, where a student layer is forced to resemble a certain teacher layer. In this work, we revisit such layer-selection strategies and observe an intriguing phenomenon that layer-selection strategy does not matter (much) in intermediate-layer matching---even seemingly nonsensical matching strategies such as *reverse matching* still result in surprisingly good student performance. We provide an interpretation for this phenomenon by examining the angles between teacher layers viewed from the student's perspective. Our work sheds light on KD practice, as layer-selection strategies may not be the main focus of KD system design and vanilla forward matching works well in most setups.

Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn’t Matter (Much)

The vast majority of the world's languages, particularly low-resource and indigenous ones like Bhili, remain critically underserved by modern language technologies. The primary bottleneck is the lack of large-scale corpora required for standard pre-training. To address this gap, we introduce cross-lingual contrastive distillation, a novel and data-efficient, compute-efficient paradigm for creating powerful language models without a massive monolingual corpus. Our method adapts a pre-existing multilingual model (MuRIL) by using a fixed, expert teacher model (HindBERT) to distill semantic knowledge from a related high-resource language (Hindi) via a contrastive objective on a modest parallel corpus. Through comprehensive experiments, we show that our resulting model, GARuD-Bhili, significantly outperforms strong zero-shot and MLM-only baselines on a suite of evaluations, including intrinsic language modeling, downstream sentiment analysis, and cross-lingual benchmarks (Tatoeba, XNLI). Our work presents a generalizable and scalable blueprint for linguistic empowerment, offering a practical pathway to develop robust language technologies for other underserved languages globally.

GARuD: Guided Alignment of Representations using Distillation for Ultra-Low-Resource Languages

Quantifying the effects of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, is challenging. Real-world interventions are often infeasible, necessitating reliance on observational data. Traditional causal inference methods, typically designed for binary or discrete treatments, are inadequate for handling the complex, high-dimensional textual data. This paper addresses these challenges by proposing CausalDANN, a novel approach to estimate causal effects using text transformations facilitated by large language models (LLMs). Unlike existing methods, our approach accommodates arbitrary textual interventions and leverages text-level classifiers with domain adaptation ability to produce robust effect estimates against domain shifts, even when only the control group is observed. This flexibility in handling various text interventions is a key advancement in causal estimation for textual data, offering opportunities to better understand human behaviors and develop effective interventions within social systems.

Estimating Causal Effects of Text Interventions Leveraging LLMs

The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts. This paper explores the potential of Large Language Models (LLMs) for classifying the political ideology of online content through in-context learning (ICL). Our extensive experiments involving demonstration selection in label-balanced fashion, conducted on three datasets comprising news articles and YouTube videos, reveal that our approach significantly outperforms zero-shot and traditional supervised methods. Additionally, we evaluate the influence of metadata (e.g., content source and descriptions) on ideological classification and discuss its implications. Finally, we show how providing the source for political and non-political content influences the LLM's classification.

"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection

Emotion recognition methods typically assign labels at the sentence level, obscuring the specific linguistic cues that signal emotion. This limits their utility in applications requiring targeted responses, such as empathetic dialogue and clinical support, which depend on knowing which language expresses emotion. The task of identifying \textit{emotion evidence} -- text spans conveying emotion -- remains underexplored due to a lack of labeled data. Without span-level annotations, we cannot evaluate whether models truly localize emotion expression, nor can we diagnose the sources of emotion misclassification. We introduce the SEER (Span-based Emotion Evidence Retrieval) Benchmark to evaluate Large Language Models (LLMs) on this task. SEER evaluates single and multi-sentence span identification with new annotations on 1200 real-world sentences. We evaluate 14 LLMs and find that, on single-sentence inputs, the strongest models match the performance of average human annotators, but performance declines in multi-sentence contexts. Key failure modes include fixation on emotion keywords and false positives in neutral text.

SEER: The Span-based Emotion Evidence Retrieval Benchmark

Shaping inclusive representations that embrace diversity and ensure fair participation and reflections of values is at the core of many conversation-based models. However, many existing methods rely on surface inclusion using mention of user demographics or behavioral attributes of social groups. Such methods overlook the nuanced, implicit expression of opinion embedded in conversations. Furthermore, the over-reliance on overt cues can exacerbate misalignment and reinforce harmful or stereotypical representations in model outputs. Thus, we took a step back and recognized that equitable inclusion needs to account for the implicit expression of opinion and use the stance of responses to validate the normative alignment. This study aims to evaluate how opinions are represented in NLP or computational models by introducing an alignment evaluation framework that foregrounds implicit, often overlooked conversations and evaluates the normative social views and discourse. Our approach models the stance of responses as a proxy for the underlying opinion, enabling a considerate and reflective representation of diverse social viewpoints. We evaluate the framework using both (i) positive-unlabeled (PU) online learning with base classifiers, and (ii) instruction-tuned language models to assess post-training alignment. Through this, we provide a basis for understanding how implicit opinions are (mis)represented and offer a pathway toward more inclusive model behavior.

EMBRACE: Shaping Inclusive Opinion Representation by Aligning Implicit Conversations with Social Norms

Social norms are implicit, culturally grounded expectations that guide interpersonal communication. Unlike factual commonsense, norm reasoning is subjective, context-dependent, and varies across cultures—posing challenges for computational models. Prior works provide valuable normative annotations but mostly target isolated utterances or synthetic dialogues, limiting their ability to capture the fluid, multi-turn nature of real-world conversations. In this work, we present Norm-RAG, a retrieval-augmented, agentic framework for nuanced social norm inference in multi-turn dialogues. Norm-RAG models utterance-level attributes including communicative intent, speaker roles, interpersonal framing, and linguistic cues and grounds them in structured normative documentation retrieved via a novel Semantic Chunking approach. This enables interpretable and context-aware reasoning about norm adherence and violation across multilingual dialogues. We further introduce MINDS (Multilingual Interactions with Norm-Driven Speech), a bilingual dataset comprising 31 multi-turn Mandarin-English and Spanish-English conversations. Each turn is annotated for norm category and adherence status using multi-annotator consensus, reflecting cross-cultural and realistic norm expression. Our experiments show that Norm-RAG improves norm detection and generalization, demonstrates improved performance for culturally adaptive and socially intelligent dialogue systems.

MINDS: A Cross-Cultural Dialogue Corpus for Social Norm Classification and Adherence Detection

Despite significant progress in large language models (LLMs), their knowledge and evaluation continue to be centered around high-resource languages, leaving critical gaps in low-resource settings. This raises questions about how effectively LLMs handle subjects that require locally relevant knowledge. To address this challenge, we need a robust dataset that reflects the knowledge of underrepresented regions such as Bangladesh. In this paper, we present ***SOMAJGYAAN***, a Bangla multiple-choice dataset consisting of 4,234 questions, annotated across five levels of difficulty. The questions are drawn from Bangladesh’s National Curriculum and Global Studies textbooks, covering a wide range of domains including History, Geography, Economics, Social Studies, Politics and Law, and Miscellaneous topics. Difficulty levels were assigned by four expert annotators to minimize annotation bias. The experiments reveal that closed-source LLMs perform better than open-source LLMs. While fine-tuning open-source models on \name improves their performance, they still fall short of matching closed-source LLMs. Our findings highlight the importance of culturally grounded evaluation datasets and task-specific adaptation to improve LLM performance in low-resource language settings.

SOMAJGYAAN: A Dataset for Evaluating LLMs on Bangla Culture, Social Knowledge, and Low-Resource Language Adaptation

Recent studies have shown that prompting large language models (LLMs) with role-playing personas can enhance their reasoning capabilities. While the benefits of role-playing personas in reasoning tasks are widely recognized, it remains uncertain whether a persona aligned with the given dataset can consistently achieve these improvements. In this work, we empirically investigate the potential drawbacks of using dataset-aligned personas (referred to as **coarsely aligned personas**) and introduce Jekyll \& Hyde, a novel framework that enhances reasoning robustness by ensembling solutions from both role-playing and neutral (non-persona) prompts.
Jekyll \& Hyde first predicts an instance-specific persona tailored to each query using an LLM, then generates answers with both persona and neutral prompts, and finally selects the superior output through an LLM-based evaluator.
Experimental results claim that across twelve widely used natural language reasoning datasets and three backbone large language models, Jekyll \& Hyde consistently outperforms single-perspective LLMs, achieving an average accuracy gain of **9.98\%** on GPT‑4.
We further demonstrate that using instance‑aligned personas yields more accurate and stable performance than using dataset-aligned personas.

Persona is a Double-Edged Sword: Rethinking the Impact of Role-play Prompts in Zero-shot Reasoning Tasks

Gaps arise between a language model's use of concepts and people's expectations. This gap is critical when LLMs generate text to help people communicate via Augmentative and Alternative Communication (AAC) tools. In this work, we introduce the evaluation task of Representation Alignment for measuring this gap via human judgment. In our study, we expand keywords and emotion representations into full sentences. We select four emotion representations: Words, Valence-Arousal-Dominance (VAD) dimensions expressed in both Lexical and Numeric forms, and Emojis.  In addition to Representation Alignment, we also measure people's judgments of the accuracy and realism of the generated sentences. While representations like VAD break emotions into easy-to-compute components, our findings show that people agree more with how LLMs generate when conditioned on English words (e.g., "angry") rather than VAD scales. This difference is especially visible when comparing Numeric VAD to words. Furthermore, we found that the perception of how much a generated sentence conveys an emotion is dependent on both the representation type and which emotion it is.

Downloads

Next from IJCNLP-AACL 2025

Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn’t Matter (Much)

Similar lecture

MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models