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Enhancing the Capability and Robustness of Large Language Models through Reinforcement Learning-Driven Query Refinement
Computation and Language · Submitted 1 Jul 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Extensive experiments demonstrate that the refinement model not only improves the quality of responses but also strengthens their robustness against jailbreak attacks.
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The abstract, sentence by sentence
The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to be brief and vague, thereby significantly limiting the full potential of LLMs. Moreover, harmful prompts can be meticulously crafted and manipulated by adversaries to jailbreak LLMs, inducing them to produce potentially toxic content. To enhance the capabilities of LLMs while maintaining strong robustness against harmful jailbreak inputs, this study proposes a transferable and pluggable framework that refines user prompts before they are input into LLMs. This strategy improves the quality of the queries, empowering LLMs to generate more truthful, benign and useful responses. Specifically, a lightweight query refinement model is introduced and trained using a specially designed reinforcement learning approach that incorporates multiple objectives to enhance particular capabilities of LLMs. Extensive experiments demonstrate that the refinement model not only improves the quality of responses but also strengthens their robustness against jailbreak attacks. Code is available at: https://github.com/Huangzisu/query-refinement .
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. | background 1.00 | 0.08 | 0.03 | asserted 1.00 |
| s1 | However, these prompts often tend to be brief and vague, thereby significantly limiting the full potential of LLMs. | background 0.87 | 0.10 | 0.02 | asserted 0.89 |
| s2 | Moreover, harmful prompts can be meticulously crafted and manipulated by adversaries to jailbreak LLMs, inducing them to produce potentially toxic content. | background 0.98 | 0.06 | 0.02 | asserted 0.86 |
| s3 | To enhance the capabilities of LLMs while maintaining strong robustness against harmful jailbreak inputs, this study proposes a transferable and pluggable framework that refines user prompts before they are input into LLMs. | contribution 0.99 | 0.75 | 0.18 | asserted 0.96 |
| s4 | This strategy improves the quality of the queries, empowering LLMs to generate more truthful, benign and useful responses. | interpretation 0.41 | 0.47 | 0.30 | asserted 1.00 |
| s5 | Specifically, a lightweight query refinement model is introduced and trained using a specially designed reinforcement learning approach that incorporates multiple objectives to enhance particular capabilities of LLMs. | method 0.98 | 0.93 | 0.06 | asserted 1.00 |
| s6 | Extensive experiments demonstrate that the refinement model not only improves the quality of responses but also strengthens their robustness against jailbreak attacks. | result 1.00 | 0.95 | 0.73 | asserted 1.00 |
| s7 | Code is available at: https://github.com/Huangzisu/query-refinement . | contribution 0.74 | 0.37 | 0.02 | asserted 0.86 |
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The paper
Original abstract
The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to be brief and vague, thereby significantly limiting the full potential of LLMs. Moreover, harmful prompts can be meticulously crafted and manipulated by adversaries to jailbreak LLMs, inducing them to produce potentially toxic content. To enhance the capabilities of LLMs while maintaining strong robustness against harmful jailbreak inputs, this study proposes a transferable and pluggable framework that refines user prompts before they are input into LLMs. This strategy improves the quality of the queries, empowering LLMs to generate more truthful, benign and useful responses. Specifically, a lightweight query refinement model is introduced and trained using a specially designed reinforcement learning approach that incorporates multiple objectives to enhance particular capabilities of LLMs. Extensive experiments demonstrate that the refinement model not only improves the quality of responses but also strengthens their robustness against jailbreak attacks. Code is available at: https://github.com/Huangzisu/query-refinement .
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3 arXiv papers cite this in 7 sentences
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Cites it as background 2 papers
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Additionally, RL based methods are designed to optimize a prompt rewritten model through reward functions and task-specific templates [ref] .
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs · Related Work · 0.98
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Common approaches include representation engineering [ref] , prompt optimization [ref] , SFT on demonstrations [ref] , direct preference optimization (DPO) from preference-pair data [ref] , and RL guided by a preference reward model [ref] .
Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction · Related Work · 0.92
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