Papers with manual tuning
LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment (2026.acl-long)
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| Challenge: | Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade-off—reducing jailbreak increases over-refusal. |
| Approach: | They propose a method which aligns va with vb through closed-form weight updates, making the model’s willingness to respond causally dependent on its safety assessment. |
| Outcome: | Experiments on 12 LLMs show that the proposed method achieves 11.45% higher F1 than the best baseline while preserving 95.92% utility. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation (2024.findings-acl)
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Siyin Wang, Shimin Li, Tianxiang Sun, Jinlan Fu, Qinyuan Cheng, Jiasheng Ye, Junjie Ye, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing decoding strategies and hyperparameters may not be optimal for each sample. |
| Approach: | They propose a model that auto-regulates decoding strategies and hyperparameters . this approach eliminates the need for extensive manual tuning, they argue . |
| Outcome: | The proposed model eliminates the need for extensive manual tuning, offering a more autonomous, self-regulate model behavior. |
Steering Language Models in Multi-Token Generation: A Case Study on Tense and Aspect (2025.emnlp-main)
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| Challenge: | Prior work has focused largely on binary grammatical contrasts, but how do they encode their syntactic knowledge internally? |
| Approach: | They propose to use a multidimensional hierarchical grammar phenomenon to identify distinct, orthogonal directions in residual space to demonstrate causal control over both grammatical features. |
| Outcome: | The proposed model can encode tense and aspect in human-like ways, but effective steering during generation is sensitive to multiple factors and requires manual tuning or automated optimization. |
DisLoRA: Task-specific Low-Rank Adaptation via Orthogonal Basis from Singular Value Decomposition (2025.emnlp-main)
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| Challenge: | Extensive experiments on GLUE and Commonsense Reasoning benchmarks demonstrate that DisLoRA surpasses established PEFT methods, including LoRA, PiSSA, DoRA, LoRA-Dash, and SORSA. |
| Approach: | They propose a framework that leverages singular value decomposition to decompose pretrained weight matrices into orthogonal backbone and task-specific subspaces. |
| Outcome: | Extensive experiments on GLUE and Commonsense Reasoning benchmarks show that DisLoRA surpasses established PEFT methods, including LoRA, PiSSA, DoRA, LoRA-Dash, and SORSA. |
SMART: Submodular Data Mixture Strategy for Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing methods for fine tuning language models are manual or rely on intuition. |
| Approach: | They propose a method which uses a submodular function to assign importance scores to tasks and then use them to determine mixture weights. |
| Outcome: | The proposed method outperforms traditional methods such as examples proportional mixing and equal mixing. |
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)
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Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. |
| Approach: | They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation . |
| Outcome: | The proposed framework improves Java-to-C# translation quality at the repository level. |
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization (2025.findings-acl)
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Amitava Das, Suranjana Trivedy, Danush Khanna, Yaswanth Narsupalli, Basab Ghosh, Rajarshi Roy, Gurpreet Singh, Vinija Jain, Vasu Sharma, Aishwarya Naresh Reganti, Aman Chadha
| Challenge: | Direct Preference Optimization (DPO) is a cornerstone for preference alignment but is constrained by fixed divergence measures and limited feature transformations. |
| Approach: | They propose a new enhancement of Direct Preference Optimization that integrates kernel methods to overcome these challenges. |
| Outcome: | The proposed model improves divergence measures and features by using kernels . the proposed model achieves state-of-the-art generalization in factuality, safety, reasoning, and instruction following . |
DPLoRA: A Dual-Pruning Framework based on ILP Optimization and Progressive Pruning for Parameter-Efficient LoRA Fine-Tuning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) require computational resources for fine-tuning. |
| Approach: | They propose a framework that optimizes rank allocation via two stages . they propose an initial pruning stage and a progressive pruning stage . |
| Outcome: | The proposed framework outperforms existing PEFT baselines on GLUE and instruction-following tasks while reducing training time and trainable parameters by over 80%. |