Papers with principled tool
Beyond Bias Scores: Unmasking Vacuous Neutrality in Small Language Models (2026.eacl-srw)
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| Challenge: | Large Language Models (LLMs) are expensive to deploy locally and can reproduce harmful social biases in high-stakes settings such as healthcare and education. |
| Approach: | They propose a multi-dimensional evaluation paradigm to assess SLM fairness prior to deployment. |
| Outcome: | The proposed framework examines model robustness across four stages - biases, utility, ambiguity handling, and positional bias over diverse social bias categories. |
Sparse Latents Steer Retrieval-Augmented Generation (2025.acl-long)
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Chunlei Xin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Xuanang Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | In this study, we uncover interpretable latents that govern RAG behavior in large language models . Sparse Autoencoders are used to control large language model (LLM) behavior . |
| Approach: | They leverage Sparse Autoencoders within the LLaMA Scope to uncover latents that govern RAG behaviors. |
| Outcome: | The proposed model can be used to control large language models without architectural modifications. |
Modeling and Solving Stable Matching under Probabilistic Preferences with Large Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have shown strong capability in understanding and simulating humans’ decisions, suggesting a new way to use LLMs as tools to study social systems. |
| Approach: | They propose a Hybrid GS–LLM matching method that integrates Gale–Shapley with probabilistic acceptance decisions. |
| Outcome: | The proposed method outperforms classical baselines in terms of stability and improves robustness under uncertainty. |
From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives? (2026.findings-acl)
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| Challenge: | large language models are often used as annotators at scale, but are not faithful estimators of human perspectives. |
| Approach: | They characterize the conditions under which large language models outperform human annotators . they find they are statistically superior frontline estimators based on low variance . |
| Outcome: | The proposed model outperforms human annotators when predicting subgroup opinions on subjective tasks. |