Papers by Tom Zehle
Can Calibration of Positional Encodings Enhance Long Context Utilization? (2026.findings-eacl)
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| Challenge: | Large language models suffer from positional biases that reduce effective utilization of long contexts. |
| Approach: | They propose a training-free framework for calibrating Positional Encodings at inference time. |
| Outcome: | The proposed framework improves on needle-in-a-haystack and cross-chunk reasoning benchmarks and provides a lightweight method for improving long-context utilization. |
promptolution: A Unified, Modular Framework for Prompt Optimization (2026.eacl-demo)
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| Challenge: | Existing implementations of prompt optimization are tied to unmaintained, isolated codebases or require invasive integration into application frameworks. |
| Approach: | They propose a unified, modular open-source framework that integrates multiple contemporary discrete prompt optimizers within a single extensible system for both practitioners and researchers. |
| Outcome: | The proposed framework integrates multiple discrete prompt optimizers, supports systematic and reproducible benchmarking, and returns framework-agnostic prompt strings, enabling seamless integration into existing LLM pipelines while remaining agnosite to the underlying model implementation. |