Papers by Kian Ahrabian
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning (2023.emnlp-main)
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| Challenge: | Temporal knowledge graphs (TKGs) are used to represent real-world facts in a structured way. |
| Approach: | They propose to use in-context learning with large language models for TKG forecasting . they compare naive LLMs to state-of-the-art (SOTA) supervised models . |
| Outcome: | The proposed approach performs well against pre-trained large language models . the proposed approach is based on simple heuristics and state-of-the-art models compared with pre-trainers . |
A Practical Analysis of Human Alignment with *PO (2025.findings-naacl)
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| Challenge: | Prior research focused on identifying the best-performing method to varying hyperparameters . prior research focused primarily on a grid search, which can be impractical for general practitioners . |
| Approach: | They propose a preference optimization method that is more stable across hyperparameters and reduces the average response length. |
| Outcome: | The proposed method increases likelihood of achieving better results through various metrics, such as KL divergence and response length. |
Structure Aware Negative Sampling in Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing methods for learning low-dimensional representations of entities and relations in knowledge graphs employing corruption distributions that generate hard negative samples. |
| Approach: | They propose a structure-aware negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node’s k-hop neighborhood. |
| Outcome: | The proposed method finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization. |
On the Adaptation of Unlimiformer for Decoder-Only Transformers (2024.lrec-main)
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| Challenge: | despite efforts in the community, most common models have a context length of 4k or less. |
| Approach: | They propose to adapt a vector-retrieval augmentation method to decoder-only transformers . they also expand the experimental setup on summarization to include a new task and an instruction-tuned model . |
| Outcome: | The proposed model performs on par with a model with 2x the context length. |
A Systematic Analysis of Base Model Choice for Reward Modeling (2025.emnlp-main)
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| Challenge: | Reinforcement learning from human feedback (RLHF) and reward modeling are key to training powerful large language models (LLMs). |
| Approach: | They propose to combine RLHF and reward modeling to boost model selection . they also demonstrate that a small set of benchmarks could be combined to boost the model selection. |
| Outcome: | The results show that the model selection can be improved by up to 14% compared to the most common (default) choice. |