Papers by Alfonso Amayuelas
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models (2024.findings-acl)
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| Challenge: | Known-unknown questions are characterized by high uncertainty due to the absence of definitive answers. |
| Approach: | They introduce a dataset with known-unknown questions and establish a categorization framework to clarify the origins of uncertainty in such queries. |
| Outcome: | The proposed model improved in distinguishing between known and unknown queries within open-ended question-answering scenarios. |
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown exceptional results when working individually, and have reduced parameter size and inference times. |
| Approach: | They evaluate the behavior of a network of models collaborating through debate under the influence of an adversary and examine inference-time methods to generate more compelling arguments. |
| Outcome: | The proposed model-based model-driven analysis shows that the model-led model-mediated debates generate more compelling arguments and provide a defensive strategy. |
Investigating the Transferability of Code Repair for Low-Resource Programming Languages (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown remarkable performance on code generation tasks. |
| Approach: | They investigate the benefits of distilling the ability to repair code for both high and low resource languages to determine if the techniques are also applicable in low resource settings. |
| Outcome: | The proposed techniques are effective in high- and low-resource languages, but weak in low-level languages. |
DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025.findings-emnlp)
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| Challenge: | Multi-agent debates can improve the accuracy of Large Language Models by having multiple agents discuss solutions over several rounds of debate. |
| Approach: | a debate framework that uses uncertainty metrics to assess agent confidence is proposed . the framework uses textual prompts or a modified attention mechanism that adjusts token weights . |
| Outcome: | The proposed framework assesses agent confidence using uncertainty metrics . the framework is available at https://github.com/lukeyoffe/debunc. |