Papers with MIRAGE
MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly in interactive role-playing contexts. |
| Approach: | They propose a framework to assess LLMs' proficiency in portraying advanced human behaviors through murder mystery games using eight intricately crafted scripts. |
| Outcome: | The framework evaluates LLMs' performance in portraying advanced human behaviors through murder mystery games. |
MIRAGE: Metadata-guided Image Retrieval and Answer Generation for E-commerce Troubleshooting (2026.eacl-industry)
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| Challenge: | Existing multimodal systems often associate text and images based on embedding similarity or simple co-location, but fail to ensure that the linked image accurately depicts the specific product or component mentioned in a troubleshooting instruction. |
| Approach: | They propose a metadata-first paradigm that treats structured metadata as a modality for multimodal grounding. |
| Outcome: | The proposed model uses a semantic schema to capture product attributes and visual aspects. |
MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation (2025.findings-naacl)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems are limited in their evaluation due to the intricate interplay between retrieval and generation components. |
| Approach: | They propose a Question Answering Question Answerer dataset specifically designed for RAG evaluation that integrates external, non-parametric knowledge retrieved by a retrieval pool of 37,800 entries. |
| Outcome: | The proposed dataset consists of 7,560 curated instances mapped to a retrieval pool of 37,800 entries, enabling an efficient evaluation of both retrieval and generation tasks. |
Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (2026.eacl-long)
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Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee
| Challenge: | Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains, but in complex environments, auxiliary guidance may be imperfect. |
| Approach: | They propose a benchmark to measure the robustness of large language models under imperfect guidance. |
| Outcome: | The proposed benchmark compared LLM agents in navigation, cooking, and gaming in a variety of environments with auxiliary guidance and noisy or underspecified instructions extracted from demonstrations. |
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation (2024.emnlp-main)
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| Challenge: | Recent research has shown that self-citing large language models (LLMs) fail to faithfully reflect their context usage throughout the generation process. |
| Approach: | They propose a plug-and-play approach using model internals for faithful answer attribution in RAG applications that detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction. |
| Outcome: | The proposed approach achieves citation quality and efficiency comparable to self-citation while allowing for a finer-grained control of attribution parameters. |
Benchmarking Retrieval-Augmented Generation for Medicine (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have state-of-the-art performance on a wide range of medical question answering tasks, but they still face challenges with hallucinations and outdated knowledge. |
| Approach: | They propose a benchmark to evaluate medical RAG systems using large-scale experiments with over 1.8 trillion prompt tokens. |
| Outcome: | The proposed benchmark improves accuracy of six different LLMs by up to 18% over chain-of-thought prompting. |