Papers with computational processes
Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features (2025.coling-industry)
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| Challenge: | Quantum computing is rapidly evolving in both physics and computer science due to its potential to solve complex quantum physics problems and accelerate computational processes. |
| Approach: | They propose to initialize node features using LLMs to enhance node representations for link prediction tasks in graph neural networks. |
| Outcome: | The proposed method compared to traditional node embedding techniques on a quantum computing semantic network and demonstrated efficacy compared with other methods. |
Do LLMs Really Know What They Don’t Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness (2026.findings-acl)
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| Challenge: | Recent work suggests that large language models (LLMs) produce hallucinated and factually correct outputs. |
| Approach: | They propose a taxonomy categorizing hallucinations into Unassociated Hallucination (UH) and Associated Hallucinian (AH) they propose to use internal signals to distinguish hallucinos from factual errors . |
| Outcome: | The proposed taxonomy categorizes hallucinations into Unassociated Hallucination (UH) and Associated Hallucinications (AHs) based on the proposed taxonomic, the authors show that hidden states reflect whether the model is recalling parametric knowledge rather than the truthfulness of the output itself. |
Surge: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors (2025.emnlp-main)
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| Challenge: | Neural surrogate models are powerful tools in data mining, but are underexplored . large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks . |
| Approach: | They propose a benchmarking framework to examine the feasibility of large language models . they examine scaling laws, data efficiency, and predictive accuracy of 21 open-source and proprietary LLMs . |
| Outcome: | The proposed benchmark examines 21 open-source and proprietary LLMs . it also examines scaling laws, data efficiency, and predictive accuracy . |
HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts (2024.acl-long)
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| Challenge: | Existing methods for enhancing performance through increased use of expert knowledge often result in diminishing sparsity during expert selection. |
| Approach: | They propose a framework that integrates the computational processes of MoE with the concept of knowledge transferring in multi-task learning. |
| Outcome: | The proposed framework outperforms existing methods under identical conditions concerning the number of experts. |