Papers with GEAR
GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution (2024.eacl-long)
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| Challenge: | Recent work on Augmented Language Models (LLMs) over-rely on task-specific demonstrations that limits their generalizability and computational cost. |
| Approach: | They propose a query-tool grounding algorithm that is generalizable to various tasks . they delegate tool grounding and execution to small language models and LLMs . |
| Outcome: | The proposed algorithm outperforms baselines on 14 datasets and shows it can be generalized to different tasks. |
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)
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| Challenge: | Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence. |
| Approach: | They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence. |
| Outcome: | The proposed framework achieves significant performance improvements on a large-scale benchmark dataset. |
GEAR: A Scalable and Interpretable Evaluation Framework for RAG-Based Car Assistant Systems (2025.emnlp-industry)
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Niloufar Beyranvand, Hamidreza Dastmalchi, Aijun An, Heidar Davoudi, Winston Chan, Ron DiCarlantonio
| Challenge: | Large language models (LLMs) increasingly power car assistants, but evaluating response quality remains a challenge. |
| Approach: | They propose a framework that uses large language models as evaluators to compare assistant responses against ground-truth counterparts. |
| Outcome: | The proposed framework compares assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. |
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |