Papers with inference-time methods
Let’s Put Ourselves in Sally’s Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models (2026.findings-eacl)
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Kazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida, Yoshihiro Yamazaki, Keita Suzuki, Hiroaki Sugiyama, Kuniko Saito
| Challenge: | Existing methods for Theory of Mind (ToM) are specialized for inferring beliefs from contexts involving changes in the world state. |
| Approach: | They propose a method which makes fewer assumptions about contexts and is applicable to broader scenarios. |
| Outcome: | The proposed method makes fewer assumptions about contexts and is applicable to broader scenarios. |
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)
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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations. |
| Approach: | They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference. |
| Outcome: | The proposed method significantly mitigates object hallucinations across various model architectures. |
Protecting multimodal large language models against misleading visualizations (2026.acl-long)
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| Challenge: | MLLMs are robust to misleading visualizations, i.e., charts that distort the underlying data, leading readers to draw inaccurate conclusions. |
| Approach: | They propose to use table-based QA and redrawing the visualization to improve QA performance on misleading visualizations. |
| Outcome: | The proposed methods improve MLLM question-answering accuracy on misleading visualizations without compromising accuracy on non-misleading ones. |
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown exceptional results when working individually, and have reduced parameter size and inference times. |
| Approach: | They evaluate the behavior of a network of models collaborating through debate under the influence of an adversary and examine inference-time methods to generate more compelling arguments. |
| Outcome: | The proposed model-based model-driven analysis shows that the model-led model-mediated debates generate more compelling arguments and provide a defensive strategy. |
Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate long-form and coherent text, yet they often hallucinate facts, which undermines their reliability. |
| Approach: | They propose a Learnable Intervention method for Truthfulness Optimization that automatically identifies the optimal intervention intensity tailored to each query context. |
| Outcome: | Experiments on multiple LLMs and question-answering datasets show that LITO improves truthfulness while preserving task accuracy. |
InsideOut: Measuring and Mitigating Insider–Outsider Bias in Interview Script Generation (2026.acl-long)
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| Challenge: | Recent research has raised concerns about culture-related fairness issues in LLM-generated content. |
| Approach: | They propose to use 4,000 generation prompts and three evaluation metrics to quantify LLMs' **insider-outsider bias** . |
| Outcome: | The proposed method reduces bias in Llama model by 89.70% and mitigates bias on Qwen by 82.54% on cultural alignment gap metric. |
Certified Mitigation of Worst-Case LLM Copyright Infringement (2025.emnlp-main)
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| Challenge: | Large language models are trained on vast datasets that include copyrighted material or content with usage restrictions. |
| Approach: | They propose a "copyright takedown" method that provides certified copyright take down . they use a combination of inference-time and rewriting techniques to transform potentially infringing segments . |
| Outcome: | The proposed method reduces infringement risk, preserves utility, and accommodates different levels of enforcement stringency with adaptive abstention. |