Papers with shared representation
Rhetorical Questions in LLM Representations: A Linear Probing Study (2026.acl-long)
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| Challenge: | Rhetorical questions are asked not to seek information, but to persuade or signal stance . how large language models internally represent rhetorical questions remains unclear . |
| Approach: | They analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts. |
| Outcome: | The results show that rhetorical signals emerge early and are most stably captured by last-token representations. |
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
| Approach: | They propose to use emotional patterns identified by clinical practitioners to enhance the prediction capabilities of a mental illness detection model built using a deep neural network architecture. |
| Outcome: | The proposed method achieves a task-specific AUC higher than 0.90 . it compares multi-task learning with multi-channel convolutional neural network and multiple inputs to methods such as multi-class classification . |
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
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| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
| Outcome: | The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively. |
HyperIDP: Customizing Temporal Hypergraph Neural Networks for Multi-Scale Information Diffusion Prediction (2025.coling-main)
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| Challenge: | Existing studies on information diffusion prediction have focused on both macroscopic and microscopic scales. |
| Approach: | They propose a hypergraph-based model that manages both macroscopic and microscopic diffusion predictions. |
| Outcome: | The proposed model outperforms baseline models on both macroscopic and microscopic tasks. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
Multi-Lingual Question Generation with Language Agnostic Language Model (2021.findings-acl)
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| Challenge: | Existing training data for question generation in English and Chinese is limited . a language-agnostic model is developed to learn the shared representation from several languages in a single architecture. |
| Approach: | They propose a language-agnostic language model which learns the shared representation from several languages in a single architecture. |
| Outcome: | The proposed model improves multi-lingual question generation over five languages. |
A Hybrid Detection and Generation Framework with Separate Encoders for Event Extraction (2023.eacl-main)
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| Challenge: | Recent work on event extraction tasks has been based on classification-based methods . a new generation-based method is being developed to extract event triggers and event arguments from plain text. |
| Approach: | They propose to use independent encoders to model event detection and event argument extraction, respectively, and use token-level features to precisely control the fusion between two encoder. |
| Outcome: | The proposed method avoids feature interference and achieves joint training . it is compared with other methods and achieved competitive results on standard benchmarks . |
GuideQ: Framework for Guided Questioning for progressive informational collection and classification (2025.findings-naacl)
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| Challenge: | Using a new multilingual dataset, we examine how LLMs can be used to represent factual knowledge across languages. |
| Approach: | They propose a methodology to measure the extent of representation sharing across languages by repurposing knowledge editing methods. |
| Outcome: | The proposed model can answer a question consistently across languages and can store the answers in a shared representation for several languages. |
Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. |
| Approach: | They evaluated five languages using a parallel corpus of 1,000 instances each and found a zero-shot improvement of 7.4 from the baseline score of 7.1 to a score of 15.5 at best. |
| Outcome: | The proposed model improves performance in the linguistically diverse country of Indonesia by 7.4 from baseline score of 7.1 to 15.5 at best. |
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning (2020.acl-main)
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| Challenge: | Recent neural network models have achieved impressive performance on sentiment classification in English and other languages. |
| Approach: | They propose an unsupervised sentiment classification model that leverages an uncontrolled machine translation system and a language discriminator to learn a shared representation. |
| Outcome: | The proposed model outperforms other models on five language pairs. |
Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing models re-encode the sentence for each aspect or rely on static use of deep representations, leading to redundant computation and limited adaptivity. |
| Approach: | They propose a single-pass inference framework that encodes each sentence once to construct a reusable, depth-ordered substrate. |
| Outcome: | Experiments show that DABS reduces end-to-end computation by 60% in multi-aspect settings. |
Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning (2026.acl-long)
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| Challenge: | Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs). |
| Approach: | They propose a framework that aligns natural language with logical symbols to establish a shared representation and reshapes training dynamics around phase-transition boundaries to progressively strengthen reasoning at increasing logical depths. |
| Outcome: | The proposed framework mitigates logical reasoning collapse at high complexity while improving generalization to unseen logical compositions. |
Filling in the Mechanisms: How do LMs Learn Filler-Gap Dependencies under Developmental Constraints? (2026.findings-acl)
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| Challenge: | Language models lack language-specific biases, yet still posit some important syntactic generalizations. |
| Approach: | They applied Distributed Alignment Search to checkpoints of a language model from the BabyLM challenge to evaluate whether representations of filler-gap dependencies transfer between wh-questions and topicalization. |
| Outcome: | The results suggest shared, yet item-sensitive mechanisms may develop with limited training data. |