Papers by Baochun Li
PepRec: Progressive Enhancement of Prompting for Recommendation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been gaining in-depth performance in natural language processing domains. |
| Approach: | They propose a training-free prompting framework that captures knowledge from content-based filtering and collaborative filtering to boost recommendation performance with LLMs. |
| Outcome: | The proposed framework outperforms traditional deep learning recommendation models and prompt-based recommendation systems on two real-world datasets. |
Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence (2026.acl-long)
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| Challenge: | Guided by Gut (GG) is an efficient self-guided TTS framework for Large Language Models (LLMs) that performs step-by-step reasoning at a low cost without any reward models or verifiers. |
| Approach: | They propose a self-guided TTS framework that enables LLMs to perform step-by-step reasoning at a low cost without any reward models or verifiers. |
| Outcome: | Empirical evaluations show that GG performs better than TTS with PRMs while reducing GPU memory usage by up to 10. |
rSIM: Incentivizing Reasoning Capabilities of LLMs via Reinforced Strategy Injection (2026.acl-long)
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| Challenge: | Existing literature on Reasoning Language Models (RLMs) focuses on the ability to integrate reasoning strategies into the chain-of-thought process, contributing to improved problem-solving accuracy. |
| Approach: | They propose a reinforced strategy injection mechanism that enables any LLM to become an RLM by employing a small planner to guide the LLM's CoT through the adaptive injection of reasoning strategies. |
| Outcome: | The proposed model outperforms existing models in mathematical, coding, and financial reasoning tasks and is generalizable. |