Papers with MATCHA
Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition (2026.findings-eacl)
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| Challenge: | Existing systems for conversational recommender systems (CRS) have strong results in movies, but games present distinct challenges . MATCHA framework provides specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking, and stronger safety. |
| Approach: | They propose a framework for conversational recommender systems that assigns specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking and risk control. |
| Outcome: | MATCHA outperforms baselines on real user request dataset, improves Hit@5 by 20%, reduces popularity bias by 24%, and achieves 97.9% adversarial defense. |
MATCHA: Matching Text via Contrastive Semantic Alignment (2026.findings-acl)
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| Challenge: | MATCHA is an automatic metric that rewards semantic agreement with a reference and penalizes contradictions. |
| Approach: | They introduce a metric that jointly rewards semantic agreement with a reference and penalizes contradictions. |
| Outcome: | The proposed metric outperforms popular metrics on eight public benchmarks compared with human annotations on question-answering, image caption generation, natural language inference, summarization, and semantic textual similarity tasks. |