Papers with LLaMA-3.1
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models (2026.acl-long)
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Ling Shi, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Yangyang Liu, Linlong Xu, Longyue Wang, Deyi Xiong, Weihua Luo
| Challenge: | Recent research in mechanistic interpretability has revealed that Large Language models contain disentangled, human-understandable components. |
| Approach: | They propose a framework that first identifies causal task features through frequency recall and interventional filtering, then selects “Feature-Resonant Data” that maximally activates task features for fine-tuning. |
| Outcome: | The proposed framework outperforms existing models on mathematical reasoning, summarization, and translation tasks while using only 50% of the data. |
On the Reliability of Psychological Scales on Large Language Models (2024.emnlp-main)
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| Challenge: | Recent research has focused on examining Large Language Models’ characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. |
| Approach: | They propose to examine the reliability of personality tests to LLMs by using psychological scales. |
| Outcome: | The proposed model can represent diverse personalities with specific prompt instructions. |
Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
| Outcome: | The proposed pipeline achieves state-of-the-art across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks. |
MPO: Multilingual Safety Alignment via Reward Gap Optimization (2025.acl-long)
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Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Existing preference learning methods for safety alignment are monolingual and struggle with noisy multilingual data. |
| Approach: | They propose a multilingual reward gaP optimization approach that leverages the well-aligned safety capabilities of the dominant language to improve safety alignment across multiple languages. |
| Outcome: | Extensive experiments on three LLMs, LLaMA-3.1, Gemma-2 and Qwen2.5, validate MPO’s efficacy in multilingual safety alignment without degrading general multilingual utility. |
AdaSteer: Your Aligned LLM is Inherently an Adaptive Jailbreak Defender (2025.emnlp-main)
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Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Activation steering offers training-free defense but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. |
| Approach: | They propose an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. |
| Outcome: | The proposed method outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility. |
Meta-Tool: Unleash Open-World Function Calling Capabilities of General-Purpose Large Language Models (2025.acl-long)
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| Challenge: | Large language models struggle with addressing diverse user inquiries in open-world tasks. |
| Approach: | They propose a plug-and-play tool retrieval system for LLMs to access external tool library and use retrieved tools to solve user's problem. |
| Outcome: | The proposed model improves on a finetuned version of LLaMA-3.1 and 2,800 dialogues and 7,361 tools spanning ten distinct test categories. |
Self-SoftCoT: A Self-Consistent Framework via Position-Aware Latent Space Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing Continuous reasoning approaches rely on external auxiliary models, resulting in complex deployment and fractured inference pipelines. |
| Approach: | They propose a self-contained framework that enables a frozen LLM to internally generate and consume latent thoughts without external assistants. |
| Outcome: | The proposed framework outperforms SoftCoT models on five reasoning benchmarks. |