Papers with systematic exploration
Exploring Language Model’s Code Generation Ability with Auxiliary Functions (2024.findings-naacl)
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| Challenge: | Auxiliary function is a useful component to improve language model’s code generation ability, but a systematic exploration of how they affect has yet to be done. |
| Approach: | They construct a human-crafted evaluation set which contains examples of two functions where one function assists the other to examine their ability in a multifaceted way. |
| Outcome: | The proposed model is underutilized to call the auxiliary function, suggesting future directions to enhance their implementation by eliciting the supplementary function call ability encoded in the models. |
ModRWKV: Transformer Multimodality in Linear Time (2025.emnlp-main)
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| Challenge: | Currently, multimodal studies are based on large language models with quadratic-complexity Transformer architectures. |
| Approach: | They propose a decoupled multimodal framework built upon the RWKV7 architecture as its LLM backbone and a lightweight architecture to achieve multi-source information fusion. |
| Outcome: | The proposed framework achieves multi-source information fusion through dynamically adaptable heterogeneous modality encoders. |
Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model (2025.emnlp-main)
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| Challenge: | Existing Large Reasoning Models have demonstrated broad application potential, yet their safety and reliability remain critical concerns. |
| Approach: | They conduct a safety evaluation of 13 MLRMs across 5 benchmarks and examine their safety performance. |
| Outcome: | The proposed model improves safety on jailbreak and safety-awareness benchmarks. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
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| Challenge: | GUI automation is a key challenge in dynamic environments. |
| Approach: | They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs. |
| Outcome: | The proposed GUI-explorer shows significant improvements over existing agents. |
SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have remarkable emergent abilities across various tasks, yet their performance on complex reasoning and planning tasks remains suboptimal. |
| Approach: | They propose a tree-search-based reasoning framework that encourages the exploration of intermediate steps and a round-scheduled strategy to manage draft model dispatching. |
| Outcome: | The proposed framework improves runtime speed and GPU memory management concurrently and handles multiple iterations for thought generation and state evaluation. |
»textklang« – Towards a Multi-Modal Exploration Platform for German Poetry (2022.lrec-1)
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Nadja Schauffler, Toni Bernhart, Andre Blessing, Gunilla Eschenbach, Markus Gärtner, Kerstin Jung, Anna Kinder, Julia Koch, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu, Lorenz Wesemann, Jonas Kuhn
| Challenge: | »textklang« aims to explore the relationship between written text and its potential and actual sonic realisation in lyric poetry . the platform will combine three modalities: the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . musical setting of lyrical poetry. |
| Approach: | They propose to combine a multi-modal corpus of German lyric poetry from the Romantic era with a platform for systematic exploration. |
| Outcome: | The platform will combine the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . a musical setting of lyric poetry. |
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content. |
| Approach: | They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety. |
| Outcome: | The proposed method improves safety metrics while maintaining utility. |
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning (2024.emnlp-main)
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| Challenge: | Despite its significance, a systematic exploration of commonsense causality is lacking. |
| Approach: | They focus on taxonomies, benchmarks, acquisition methods, qualitative reasoning, and quantitative measurements in commonsense causality. |
| Outcome: | The proposed method synthesizes insights from over 200 representative articles and provides a practical guide for beginners. |
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable multi-step reasoning capabilities, but they still face challenges in complex logical reasoning. |
| Approach: | They propose an algorithm-guided search framework that automates structured proof exploration and ensures logical coherence. |
| Outcome: | The proposed framework outperforms o3-mini and chain-of-thought with average gains of 23.6% and 12.5% on five datasets. |
C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs (2026.findings-acl)
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| Challenge: | Existing approaches to solve large language models address stereotypical and structural biases in isolation . however, prior paradigms address these in isolation, often at the expense of exacerbating the other . |
| Approach: | They propose a framework to tackle latent spurious feature correlations within input that drive erroneous reasoning shortcuts. |
| Outcome: | The proposed framework mitigates stereotypical and structural biases while preserving robust general reasoning capabilities. |
When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents (2026.acl-long)
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Jiahe Guo, Xiangran Guo, Yulin Hu, Zimo Long, Xingyu Sui, Xuda Zhi, Yongbo Huang, Hao He, Weixiang Zhao, Yanyan Zhao, Bing Qin
| Challenge: | Existing research on personalized LLM agents focuses on the effectiveness of personalized responses. |
| Approach: | They propose a benchmark to quantify intent legitimation in personalized interactions . they propose 'detection-reflection' method that detects intent legititimation from internal representation space . |
| Outcome: | The proposed method reduces safety degradation by using internal representation space. |
Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints (2026.findings-acl)
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| Challenge: | a new framework for evaluation of exhaustive search capabilities is needed . high-entropy enumeration tasks make such ground truth impossible for humans to create . VERITAS is a framework built on the principle of computationally irreducible constraints . |
| Approach: | They propose a framework that uses non-optimizable constraints to create verifiable searches . VERITAS can generate infinite number of test cases with perfect ground truth and precise difficulty control . |
| Outcome: | a new evaluation framework for large language models is based on non-optimizable constraints . the framework can generate infinite number of test cases with perfect ground truth and precise difficulty control . |
Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)
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| Challenge: | Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes. |
| Approach: | They propose to use a 10.7M caption dataset to compare ALP with captioning . they find that contrastive learning yields competitive, transferable representations . |
| Outcome: | The proposed model yields competitive, transferable representations, while captioning exhibits better scalability. |