Papers with contextual alignment
SkiLLens: Recognising and Mapping Novel Skills from Millions of Job Ads Across Europe Using Language Models (2026.eacl-industry)
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| Challenge: | Online job ads (OJAs) provide a real-time view of changing demands but require first retrieving skill mentions from unstructured text and then solving the entity linking problem of connecting them to standardized skill taxonomies. |
| Approach: | They propose a multilingual human-in-the-loop pipeline that extracts candidate skills from national OJA corpora using country-specific word embeddings. |
| Outcome: | The proposed pipeline enables timely, multilingual monitoring of emerging skills, supporting agile policy-making and targeted training initiatives. |
RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion (2026.eacl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. Large Language Models (LLMs) have sparked interest in using pretrained generative models for TKG completion. |
| Approach: | They propose a framework that allows for rule-based multi-hop sampling and contrastive fine-tuning to shape relational compatibility. |
| Outcome: | Experiments show that RECIPE-TKG outperforms prior LLM-based methods across input regimes. |
MarathiEmoExplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource Marathi (2025.findings-emnlp)
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| Challenge: | Marathi is the third most widely spoken language in India with over 83 million native speakers . available Marath datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations. |
| Approach: | They propose to annotate Marathi sentences labeled with sentiment, emotion and a corresponding natural language justification. |
| Outcome: | The proposed dataset provides a benchmark for future research in multilingual and explainable NLP. |
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards (2025.emnlp-main)
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| Challenge: | Existing approaches to role-playing language models rely on prompt engineering or supervised fine-tuning to emulate character behaviors but neglect the underlying cognitive mechanisms driving these behaviors. |
| Approach: | They propose a novel RPLA adopting a cognize-then-respond reasoning paradigm that leverages dual cognition for more contextually grounded and psychologically coherent responses. |
| Outcome: | The proposed RPLA outperforms baselines and generalizes effectively across diverse role-playing tasks. |
Mitigating Safety Context Amnesia in Multimodal Reasoning Models via Intent-Guided Safety Reasoning (2026.acl-long)
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| Challenge: | Recent advances in Multimodal Large Reasoning Models (MLRMs) have enabled explicit chain-of-thought inference across vision and language, improving performance on complex cognitive tasks. |
| Approach: | They propose an inference-time defense that uses a percept decoupler to extract objective visual evidence into a structured intent output and a cognitive arbiter to enforce explicit safety constraints prior to generation. |
| Outcome: | The proposed defense improves defense success rates by over 62% compared to baselines while preserving task utility. |