Papers with alignment objective
Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts (2025.naacl-long)
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| Challenge: | Existing methods for aligning large language models with human preferences are poor in extensibility and require significant retraining. |
| Approach: | They propose a multi-objective alignment approach that constructs an expert prompt and an adversarial prompt for each alignment objective to contrast at the decoding time. |
| Outcome: | The proposed approach is superior to existing methods in obtaining a well-distributed Pareto front among different alignment objectives. |
Dynamic Knowledge Distillation for Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Existing methods conduct knowledge distillation statically, e.g., student model aligns output distribution to teacher model on pre-defined training dataset. |
| Approach: | They propose a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency . they find it is promising and provide discussions on potential future directions towards more efficient methods . |
| Outcome: | The proposed method can boost student model performance while accelerating training . the proposed method reduces memory usage and accelerates model inference . |
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering (2024.findings-acl)
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| Challenge: | Domain-specific question answering (QA) requires a comprehensive understanding of a specific domain to answer specialized questions. |
| Approach: | They propose a new alignment objective to align the LLM preference with different human preferences uniformly to optimize LLM performance in real-world, domain-specific QA settings. |
| Outcome: | The proposed pipeline is superior for real-scenario domain-specific question answering with LLMs. |
Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. |
| Approach: | They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective. |
| Outcome: | The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs. |
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings (2021.findings-emnlp)
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| Challenge: | Recent studies suggest different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. |
| Approach: | They propose to use Optimal Transport as an alignment objective during fine-tuning to improve multilingual contextualized representations for downstream cross-lingual transfer. |
| Outcome: | The proposed method achieves better performance on two tasks (XNLI and XQuAD) and is competitive with existing methods. |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings (2022.coling-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings are sense agnostic . a novel framework to align contextual embeddables at the sense level is proposed . |
| Approach: | They propose a framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. |
| Outcome: | The proposed framework improves word sense disambiguation tasks by leveraging bilingual dictionaries . compared with baseline results, the proposed models achieve 0.52%, 2.09% and 1.29% performance improvements . |
Pedagogical Alignment of Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are often used without pedagogical fine-tuning and provide immediate answers rather than guiding students through the problem-solving process. |
| Approach: | They propose a method for constructing large-scale preference datasets using synthetic data generation techniques that eliminates the need for manual annotation. |
| Outcome: | The proposed methods outperform standard supervised fine-tuning (SFT) and improve alignment accuracy by 13.1% and 8.7% respectively. |
Multi-Attribute Steering of Language Models via Targeted Intervention (2025.acl-long)
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| Challenge: | Existing approaches for steering large language models fail to scale to multi-attribute settings with conflicts, such as enhancing helpfulness while also reducing toxicity. |
| Approach: | They propose a steering framework for selective token-level intervention across multiple attributes that enforcing sparsity and orthogonality among vectors for different attributes. |
| Outcome: | The proposed framework outperforms existing ITI and parameter-efficient fine-tuning approaches across question answering tasks and generative tasks. |