Papers with training-based approaches
Style Vectors for Steering Generative Large Language Models (2024.findings-eacl)
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Kai Konen, Sophie Jentzsch, Diaoulé Diallo, Peer Schütt, Oliver Bensch, Roxanne El Baff, Dominik Opitz, Tobias Hecking
| Challenge: | Large language models (LLMs) can be trained on vast corpora and can generate text in a nuanced and parameterisable way. |
| Approach: | They propose to add style vectors to the activations of hidden layers during text generation to steer output towards specific styles. |
| Outcome: | The proposed approach differs from prompt engineering in that it can be nuanced and parameterisable. |
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales (2024.emnlp-main)
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| Challenge: | Existing approaches to elicit confidence from large language models are limited to binary or inaccurate group-level confidence estimates. |
| Approach: | They propose a training framework that teaches LLMs to express more fine-grained confidence estimates. |
| Outcome: | The proposed training framework reduces the confidence calibration error and maintains the performance of the model. |
Feedback Adaptation for Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing evaluation protocols focus on overall accuracy and fail to capture how systems adapt after feedback is introduced. |
| Approach: | They propose to use feedback adaptation as a problem setting for RAG systems . they propose a minimal inference-time instantiation that incorporates feedback without retraining . |
| Outcome: | The proposed evaluations show that training-based approaches exhibit a trade-off between delayed correction and reliable adaptation. |
SSSD: Simply-Scalable Speculative Decoding (2026.acl-long)
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Michele Marzollo, Jiawei Zhuang, Niklas Roemer, Niklas Zwingenberger, Lorenz K Muller, Lukas Cavigelli
| Challenge: | Existing methods for accelerating inference in Large Language Models require additional training and training, resulting in a higher deployment and maintenance cost. |
| Approach: | They propose a training-free method that combines lightweight n-gram matching with hardware-aware speculation. |
| Outcome: | SSSD reduces latency by up to 2.9 and is faster than autoregressive decoding methods. |