Papers with Self-Reflection
Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024 (2024.emnlp-main)
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| Challenge: | In light of the recent 2024 European Parliament elections, we are investigating if Large Language Models (LLMs) can be used as Voting Advice Applications (VAAs). |
| Approach: | They audit MISTRAL and MIXTRAL models and evaluate their accuracy in predicting the stance of political parties based on the latest “EU and I” voting assistance questionnaire. |
| Outcome: | The proposed models are highly accurate with an 82% accuracy on average with a significant performance disparity across political groups (50-95%). |
Self-Reflection Improves Safety of Large Reasoning Models (2026.findings-acl)
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| Challenge: | Existing safety alignment methods are shallow and do not address deeper risks and attacks in reasoning processes. |
| Approach: | They propose a technique that introduces a special Self-Reflection token to enable LRMs to perform self-reflection during generation and recover from harmful outputs. |
| Outcome: | The proposed approach outperforms the baseline model in terms of safety and helpfulness, and significantly improves model safety without adversarial training. |
Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing LLM reliability suffer from inefficient information aggregation and rigid reasoning schemes. |
| Approach: | They propose a method that explicitly models external knowledge integration capabilities by explicitly modeling knowledge relationships. |
| Outcome: | The proposed method outperforms existing methods in multiple graph reasoning tasks. |
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on tau-bench (2025.findings-emnlp)
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Venkatesh Mishra, Amir Saeidi, Satyam Raj, Mutsumi Nakamura, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral
| Challenge: | Recent advances in reasoning and planning capabilities of large language models have enabled their potential as autonomous agents capable of tool use in dynamic environments. |
| Approach: | They propose an input-reformulation multi-agent framework that reformulates user queries . |
| Outcome: | The proposed framework outperforms ReAct, Function Calling, and Self-Reflection in overall pass5 scores. |
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection (2026.acl-long)
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| Challenge: | Existing methods for multimodal stance detection face contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. |
| Approach: | They propose a multi-agent framework that integrates Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, Reasoning-Enhanced Debate stage and Self-Reflection for robust adjudication. |
| Outcome: | Extensive experiments on five datasets show that the proposed framework outperforms state-of-the-art methods. |