Papers with OC
Neuro-Symbolic Agentic Reinforcement Learning for Long-Term Original Character Companionship and Interaction (2026.acl-short)
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| Challenge: | Existing LLM-based agents that are optimized by prompting or supervised fine-tuning exhibit a generalization gap in long-horizon, socially rich interactions. |
| Approach: | They propose a framework that formalizes OC companion agents’ interactions as a POMDP and decomposes the agent into three sub-policies optimized via closed-loop RL from AI feedback with verifiable rewards in a graph-constrained action space. |
| Outcome: | The proposed framework formalizes OC companion agents’ interactions as a POMDP and decomposes the agent into three sub-policies (Router, Memory, and Persona) with verifiable rewards in a graph-constrained action space. |
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification (2021.acl-long)
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| Challenge: | Ordinal Classification (OC) tasks require ordinal classes, not nominal ones, to be evaluated. |
| Approach: | They use data from the SemEval and NTCIR communities to clarify evaluation measures for Ordinal Classification and Ordinal Quantification tasks. |
| Outcome: | The evaluation measures for Ordinal Classification (OC) and Ordinal Quantification (OQ) tasks are ordinal, not nominal. |
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques (2024.findings-acl)
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Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Pattisapu Priyatam, Anish Bhanushali, Prasanna Srinivasa Murthy
| Challenge: | Ordinal classification (OC) is a key task in natural language processing with applications in various domains such as sentiment analysis, rating prediction, and more. |
| Approach: | They propose to tackle ordinal classification (OC) through the implicit semantics of the labels . they propose to use a classical explicit approach and an implicit approach that organically engages the semantics. |
| Outcome: | The proposed methods are based on pre-trained language models and offer strategic recommendations based upon specific settings. |
“Where Does This Strange Smell Come from?”: Enabling Conversational Interfaces for Artificial Olfaction (2025.findings-emnlp)
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| Challenge: | Existing Artificial Olfaction (AO) systems are not compatible with smart home scenarios due to diverse obstacles and the need for natural interaction. |
| Approach: | They propose to use large language models to train a CIAO system for Odor Classification and Odor Source Localization in smart home scenarios. |
| Outcome: | The proposed system outperforms existing systems in indoor event detection scenarios. |
Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset (2026.findings-acl)
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| Challenge: | Clinical dialogue systems can enhance patient education and follow-up care by providing brief and subjective messages that lack critical clinical context. |
| Approach: | They propose a safety-aware dialogue system that applies information-gain guided clarification before RAG-based response generation and screens user utterances for emotional distress and suicidal ideation. |
| Outcome: | The proposed system improves quality and clinical appropriateness relative to strong baselines while aligning with expert judgments on clinically concerning utterances. |
Over-Generation and Compaction: A Prompting Strategy for Procedural Text Adaptation with Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing prompting strategies for large language models often yield superficial or erroneous adaptations due to alignmentinduced biases and the inherent complexity of procedural editing. |
| Approach: | They propose an overgenerationandcompaction prompting strategy that leverages the model’s latent knowledge and compacts them into concise, coherent adaptations. |
| Outcome: | The proposed approach improves adaptation consistency and feasibility compared to baseline prompting methods without additional fine-tuning or curated training resources. |