India

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.

IJCNLP-AACL 2025

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

hyperred

relational triple extraction

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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### 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.

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.

Interpreting Multi-Attribute Confounding through Numerical Attributes in Large Language Models

Vision-language Models (VLMs) have made significant strides in visual understanding and query response generation, but often face challenges of high computational cost and inference latency due to autoregressive decoding. In this work, we introduce an imitation-learning-based Self-Speculative Decoding (SSD) framework, named FastVLM, to address these limitations. Our approach employs a lightweight draft model for token generation in an autoregressive manner, while a full model verifies these tokens non-autoregressively. Accepted tokens proceed seamlessly, while rejected tokens are corrected by the full model and used to guide the draft model’s refinement. Through an imitation network, FastVLM enhances the draft model by integrating deeper-level insights from the full model's architecture. Also, it maintains the performance integrity of the full model while training the draft model, achieving a balance between efficiency and accuracy. Our method speeds up the inference process by $1.55-1.85\times$ as compared to the final layer with minimal loss in performance. The source code is available at https://github.com/Div290/SSD.

FastVLM: Self-Speculative Decoding for Fast Vision-Language Model Inference

In the deployment of Large Language Models (LLMs), ``spurious correctness''---where answers are correct but reasoning contains errors---poses a critical risk by creating an illusion of reliability. While prior work on LLM confidence estimation focuses on answer-level or entire reasoning path confidence, these coarse-grained approaches fail to identify which specific parts of the reasoning contain errors. We propose a fine-grained confidence estimation framework that computes confidence scores for individual evidence triplets within reasoning chains, enabling precise localization of errors. Using carefully designed prompts, we generate answers, evidence in triplet format, and their respective confidence scores simultaneously, allowing automatic detection of spurious correctness patterns where partial evidence contains factual errors. Evaluated on both Japanese and English multi-hop QA benchmarks across multiple models from three model families representing different architectures and training approaches, we show that our approach exhibits superior calibration performance for evidence confidence and demonstrates effective ability to detect spurious correct answers (up to 0.84 on our primary discrimination metric). The consistent improvements across languages demonstrate the generalizability of our method. As a secondary benefit, joint generation of confidence scores improves answer confidence calibration by up to 43\%. This prompt-based approach requires no model retraining and is immediately applicable to existing LLMs.

Fine-grained Confidence Estimation for Spurious Correctness Detection in Large Language Models

Multilingual generation with large language models (LLMs) is often of poor quality for mid- to low-resource languages, but the causes for this are not well-understood. We first demonstrate the existence of an implicit task-solving→translation pipeline for generation, whereby the model first solves the required task in a largely target-language-agnostic manner, and subsequently translates answer concepts into the intended target language. We hypothesize that the failure of the translation stage, despite task-solving success, is an important culprit for the observed low quality of final outputs, and formalize this as the translation barrier hypothesis. We quantify the extent to which either stage in the pipeline is responsible for final failure for a word translation task across 108 language pairs, and find that the translation barrier explains a dominant portion of error for a majority of language pairs, and is especially severe for low-resource target languages. Our results highlight an important bottleneck for end-to-end multilingual generation, relevant for future work seeking to improve multilinguality in LLMs.

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

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