India

The potential of Large Language Models (LLMs) as instruments for measuring social phenomena is constrained by the methodological limitations of current probing techniques. Prevailing methods rely on static, handcrafted probe sets whose quality is highly dependent on their authors&#39; subjective expertise. This results in measurement tools with inconsistent statistical reliability that defy systematic optimization. Such an &quot;artisanal&quot; approach, akin to using an &quot;uneven ruler,&quot; undermines the scientific rigor of its findings and severely limits the applicability of LLMs in the social sciences. To elevate bias measurement from a craft to a science, we introduce the Psychometric-driven Probe Optimization (PMPO) framework. This framework treats a probe set as an optimizable scientific instrument and, for the first time, utilizes a Neural Genetic Algorithm that leverages a powerful LLM as a &quot;neural genetic operator.&quot; Through a hybrid strategy of gradient-guided mutation and creative rephrasing, PMPO automatically enhances the probe set&#39;s reliability, sensitivity, and diversity. We first establish the external validity of our foundational measurement method (PLC), demonstrating a high correlation between its measurement of occupational gender bias and real-world U.S. Bureau of Labor Statistics data (average Pearson&#39;s r=0.83, p&lt;.001). Building on this, we show that the PMPO framework can elevate a standard probe set&#39;s internal consistency (Cronbach&#39;s Alpha) from 0.90 to an exceptional 0.96 within 10 generations. Critically, in a rigorous, double-blind &quot;Turing Test,&quot; probes evolved by PMPO from non-expert seeds were judged by sociology experts to have achieved a level of quality, sophistication, and nuance that is comparable to, and even indistinguishable from, those handcrafted by domain experts. This work provides a systematic pathway to upgrade LLM measurement tools from artisanal artifacts to automated scientific instruments, offering an unprecedented and trustworthy tool for AI safety auditing and computational social science.

IJCNLP-AACL 2025

PMPO: A Self-Optimizing Framework for Creating High-Fidelity Measurement Tools for Social Bias in Large Language Models

propositional likelihood comparison

neural genetic algorithm

psychometric probe optimization

**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 potential of Large Language Models (LLMs) as instruments for measuring social phenomena is constrained by the methodological limitations of current probing techniques. Prevailing methods rely on static, handcrafted probe sets whose quality is highly dependent on their authors' subjective expertise. This results in measurement tools with inconsistent statistical reliability that defy systematic optimization. Such an "artisanal" approach, akin to using an "uneven ruler," undermines the scientific rigor of its findings and severely limits the applicability of LLMs in the social sciences. To elevate bias measurement from a craft to a science, we introduce the Psychometric-driven Probe Optimization (PMPO) framework. This framework treats a probe set as an optimizable scientific instrument and, for the first time, utilizes a Neural Genetic Algorithm that leverages a powerful LLM as a "neural genetic operator." Through a hybrid strategy of gradient-guided mutation and creative rephrasing, PMPO automatically enhances the probe set's reliability, sensitivity, and diversity. We first establish the external validity of our foundational measurement method (PLC), demonstrating a high correlation between its measurement of occupational gender bias and real-world U.S. Bureau of Labor Statistics data (average Pearson's r=0.83, p<.001). Building on this, we show that the PMPO framework can elevate a standard probe set's internal consistency (Cronbach's Alpha) from 0.90 to an exceptional 0.96 within 10 generations. Critically, in a rigorous, double-blind "Turing Test," probes evolved by PMPO from non-expert seeds were judged by sociology experts to have achieved a level of quality, sophistication, and nuance that is comparable to, and even indistinguishable from, those handcrafted by domain experts. This work provides a systematic pathway to upgrade LLM measurement tools from artisanal artifacts to automated scientific instruments, offering an unprecedented and trustworthy tool for AI safety auditing and computational social science.

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.

Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations,  we introduce SurveyGen-I, an automatic survey generation framework that combines coarse-to-fine retrieval, adaptive planning, and memory-guided generation. SurveyGen-I performs survey-level retrieval to construct the initial outline and writing plan, then dynamically refines both during generation through a memory mechanism that stores previously written content and terminology, ensuring coherence across subsections. When the system detects insufficient context, it triggers fine-grained subsection-level retrieval. During generation, SurveyGen-I leverages this memory mechanism to maintain coherence across subsections. Experiments across six scientific domains demonstrate that SurveyGen-I consistently outperforms previous works in content quality, consistency, and citation coverage. The code is available at https://github.com/SurveyGens/SurveyGen-I.

SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing

While state-of-the-art large language models find high rates of success on text classification tasks such as sentiment analysis, they still exhibit vulnerabilities to adversarial examples: meticulously crafted perturbations of input data that guide models into making false predictions. These adversarial attacks are of particular concern when the systems in question are user-facing. While many attacks are able to reduce the accuracy of Transformer-based classifiers by a substantial margin, they often require a large amount of computational time and a large number of queries made to the attacked model, which makes the attacks susceptible to detection. In this work, we resolve the limitations of high query counts and necessary computational time by proposing a query-efficient word-level attack that is fast during deployment and does not compromise the attack success rate of state-of-the-art methods. Our attack constructs a dictionary of adversarial word substitutions based on prior data and leverages these substitutions to flip the sentiment classification of the text. Our attack method achieves an average of 27.49 queries—over 30% fewer than the closest competitor—while maintaining a 99.70% attack success rate. We also develop an effective defense strategy inspired by our attack approach.

Minimizing Queries, Maximizing Impact: Adaptive Score-Based Attack and Defense for Sentiment Analysis

Hyper-relational fact extraction involves identifying relational triples along with additional contextual information—known as qualifiers—such as time, location, or quantity. These qualifiers enable models to represent complex real-world knowledge more accurately. While numerous end-to-end models have been developed for extracting relational triples, they are not designed to handle qualifiers directly. In this work, we propose a straightforward and effective approach to extend existing end-to-end triple extraction models to also capture qualifiers. Our method reformulates qualifiers as new relations by computing the Cartesian product between qualifiers and their associated relations. This transformation allows the model to extract qualifier information as additional triples, which can later be merged to form complete hyper-relational facts. We evaluate our approach using multiple end-to-end triple extraction models on the HyperRED dataset and demonstrate its effectiveness in extracting hyper-relational facts.

Are Relational Triple Extraction Frameworks Sufficient for Hyper-relational Facts ?

Analysing the generalisation capabilities of relation extraction (RE) models is crucial for assessing whether they learn robust relational patterns or rely on spurious correlations. Our cross-dataset experiments find that RE models struggle with unseen data, even within similar domains. Notably, higher intra-dataset performance does not indicate better transferability, instead often signaling overfitting to dataset-specific artefacts. Our results also show that data quality, rather than lexical similarity, is key to robust transfer, and the choice of optimal adaptation strategy depends on the quality of data available: while fine-tuning yields the best cross-dataset performance with high-quality data, few-shot in-context learning (ICL) is more effective with noisier data. However, even in these cases, zero-shot baselines occasionally outperform all cross-dataset results. Structural issues in RE benchmarks, such as single-relation per sample constraints and non-standardised negative class definitions, further hinder model transferability. We release our dataset splits with sample IDs and code for reproducibility.

Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE

Search queries with superlatives (e.g., best, most popular) require comparing candidates across multiple dimensions, demanding linguistic understanding and domain knowledge. We show that LLMs can uncover latent intent behind these expressions in e-commerce queries through a framework that extracts structured interpretations or \textit{hints}. Our approach decomposes queries into attribute-value hints generated concurrently with retrieval, enabling efficient integration into the ranking pipeline. Our method improves search performanc eby 10.9 points in MAP and ranking by 5.9 points in MRR over baselines. Since direct LLM-based reranking faces prohibitive latency, we develop an efficient approach transferring superlative interpretations to lightweight models. Our findings provide insights into how superlative semantics can be represented and transferred between models, advancing linguistic interpretation in retrieval systems while addressing practical deployment constraints.

Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition

Multihop Question Answering (QA) requires systems to identify and synthesize information from multiple text passages. While most prior retrieval methods assist in identifying relevant passages for QA, further assessing the utility of the passages can help in removing redundant ones, which may otherwise add to noise and inaccuracies in the generated answers. Existing utility prediction approaches model passage utility independently, overlooking a critical aspect of multi-hop reasoning, that the utility of a passage can be context-dependent, influenced by its relation to other passages—whether it provides complementary information, or forms a crucial link in conjunction with others. In this paper, we propose a light-weight approach to model contextual passage utility, accounting for inter-passage dependencies. We fine-tune a small transformer-based model to predict passage utility scores for multihop QA. We leverage the reasoning traces from an advanced reasoning model to capture the order in which passages are used to answer a question, to obtain synthetic training data. Through comprehensive experiments, we demonstrate that our utility-based scoring of retrieved passages leads to better reranking and downstream task performance compared to relevance-based reranking methods.

Modeling Contextual Passage Utility for Multihop Question Answering

Large language models (LLMs) have significantly advanced generative applications in natural language processing (NLP). Recent trends in model architectures revolve around efficient variants of transformers or state-space/gated-recurrent models (SSMs, GRMs). However, prevailing SSM/GRM-based methods often emulate only a single attention head, potentially limiting their expressiveness. In this work, we propose MossNet, a novel mixture-of-state-space-experts architecture that emulates a linear multi-head attention (MHA). MossNet leverages a mixture-of-experts (MoE) implementation not only in channel-mixing multi-layered perceptron (MLP) blocks but also in the time-mixing SSM kernels to realize multiple "attention heads." Extensive experiments on language modeling and downstream evaluations show that MossNet outperforms both transformer- and SSM-based architectures of similar model size and data budgets. Larger variants of MossNet, trained on trillions of tokens, further confirm its scalability and superior performance. In addition, real-device profiling on a Samsung Galaxy S24 Ultra and an Nvidia A100 GPU demonstrate favorable runtime speed and resource usage compared to similarly sized baselines. Our results suggest that MossNet is a compelling new direction for efficient, high-performing recurrent LLM architectures.

MossNet: Mixture of State-Space Experts is a Multi-Head Attention

The success of Transformer language models is widely credited to their dot-product attention mechanism, which interweaves a set of key design principles: mixing information across positions (enabling multi-token interactions), sequence-dependent activations (where attention weights adapt to each input), a specific mathematical form (dot-product similarities plus softmax weighting), and coupling of queries and keys to evolving hidden states (grounding attention in the current layer). However, the necessity of each of these principles remains largely untested. In this work, we systematically deconstruct attention by designing controlled variants that selectively relax these principles, applied both uniformly across all layers and in hybrid architectures where only some layers retain standard attention. Our empirical analysis reveals that mechanisms for mixing tokens are indispensable, as their absence collapses models to near-random behavior, while the exact mathematical form and sequence dependency can be substantially relaxed, especially when preserved in just a subset of layers. Surprisingly, even variants that fail in isolation can achieve robust performance when interleaved with standard attention, highlighting a cooperative effect. These findings deepen our understanding of what truly underpins attention's effectiveness and open new avenues for simplifying language models without sacrificing performance.

Deconstructing Attention: Investigating Design Principles for Effective Language Modeling

Generating rational and generally accurate responses to tasks, often accompanied by example demonstrations, highlights Large Language Model's (LLM's) remarkable In-Context Learning (ICL) capabilities without requiring updates to the model's parameter space. Despite having an ongoing exploration focused on the inference from a document-level concept, its behavior in learning well-defined functions or relations in context needs a careful investigation. In this article, we present the performance of ICL on partially ordered relation by introducing the notion of inductively increasing complexity in prompts. In most cases, the saturated performance of the chosen metric indicates that while ICL offers some benefits, its effectiveness remains constrained as we increase the complexity in the prompts even in presence of sufficient demonstrative examples. The behavior is evident from our empirical findings and has further been theoretically justified in term of its implicit optimization process.

The code is available \href{https://github.com/DDuttaGit/ICLonPartialOrder}{here}.

Assessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation

Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize that numerical attributes occupy shared latent subspaces and investigate two questions: (1) How do LLMs internally integrate multiple numerical attributes of a single entity? (2) How does irrelevant numerical context perturb these representations and their downstream outputs? To address these questions, we combine linear probing with partial correlation analysis and prompt-based vulnerability tests across models of varying sizes. Our results show that LLMs encode real-world numerical correlations but tend to systematically amplify them. Moreover, irrelevant context induces consistent shifts in magnitude representations, with downstream effects that vary by model size. These findings reveal a vulnerability in LLM decision-making and lay the groundwork for fairer, representation-aware control under multi-attribute entanglement.

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SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing

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