Papers with RAP
RAP: A Metric for Balancing Repetition and Performance in Open-Source Large Language Models (2025.naacl-long)
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| Challenge: | Large Language Models generate repetitive content, leading to incomplete or fragmented responses, which can negatively affect user experience. |
| Approach: | They propose a new evaluation metric that quantifies and integrates repetition penalty into the assessment of model performance, enabling tuning of RPP. |
| Outcome: | The proposed evaluation metric reduces repetition while minimizing performance loss. |
Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search (2026.findings-acl)
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| Challenge: | Large language models excel at tasks such as mathematical and commonsense reasoning, but their performance improves further when additional test-time compute is allocated. |
| Approach: | They propose a plug-in framework that decides when to branch during search instead of expanding at every step. |
| Outcome: | The proposed framework reduces token generation, model calls, and runtime by 75-85% on GSM8K and Math500, with negligible or no accuracy loss. |
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)
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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li
| Challenge: | Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. |
| Approach: | They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone . |
| Outcome: | Experiments on four TVR datasets show that the proposed method performs better than other methods. |
Reasoning with Language Model is Planning with World Model (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. |
| Approach: | They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) |
| Outcome: | The proposed framework repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) it achieves optimum balance between exploration and exploitation, while achieving high-reward reasoning paths efficiently. |
New Intent Discovery with Attracting and Dispersing Prototype (2024.lrec-main)
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| Challenge: | Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype . |
| Approach: | They propose a robust and adaptive prototypical learning framework for globally distinct decision boundaries for both known and new intent categories. |
| Outcome: | The proposed method improves on CLINC, BANKING, and StackOverflow benchmarks on three challenging benchmarks. |