Papers with evaluation pipeline
Understanding the Repeat Curse in Large Language Models from a Feature Perspective (2025.findings-acl)
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| Challenge: | Large language models suffer from repetitive text generation, a phenomenon we refer to as the ”Repeat Curse”. |
| Approach: | They propose a method to induce and analyze the Repeat Curse in large language models by using mechanistic interpretability. |
| Outcome: | The proposed method induces and analyzes the Repeat Curse in large language models using mechanistic interpretability. |
LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)
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| Challenge: | Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA). |
| Approach: | They propose to conduct the first feasibility analysis to explore the viability of Large Language Models for RE by investigating their robustness to various RE scenarios stemming from data-specific characteristics. |
| Outcome: | The proposed models are robust to various RE scenarios stemming from data-specific characteristics, but their performance is not yet fully understood. |
HPSS: Heuristic Prompting Strategy Search for LLM Evaluators (2025.findings-acl)
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Bosi Wen, Pei Ke, Yufei Sun, Cunxiang Wang, Xiaotao Gu, Jinfeng Zhou, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Existing efforts to optimize text evaluation prompts neglect the combinatorial impact of multiple factors, leading to insufficient optimization of the evaluation pipeline. |
| Approach: | They propose to integrate 8 key factors for evaluation prompts and integrate them into an algorithm that searches for well-behaved prompting strategies for LLM evaluators. |
| Outcome: | The proposed method outperforms existing methods and human-designed evaluation prompts on four evaluation tasks. |