Papers by Krisztian Flautner
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat (2025.acl-long)
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| Challenge: | Evaluating large language models (LLMs) is a complex task. Pairwise ranking has emerged as state-of-the-art method to evaluate human preferences. |
| Approach: | They propose to use pairwise ranking to evaluate human preferences . they propose to evaluate the robustness of ranking algorithms in LLMs . |
| Outcome: | The proposed methods are based on the principles of effective ranking and the robustness of several ranking algorithms in the context of LLMs. |
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG (2025.emnlp-industry)
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Savini Kashmira, Jayanaka L. Dantanarayana, Joshua Brodsky, Ashish Mahendra, Yiping Kang, Krisztian Flautner, Lingjia Tang, Jason Mars
| Challenge: | Retrieval-Augmented Generation (RAG) relies on query-chunk text-to-text similarity in the embedding space for retrieval, can fail to capture deeper semantic relationships across chunks, is highly sensitive to chunking strategies, and is prone to hallucinations. |
| Approach: | They propose a graph-based retrieval framework that first constructs the knowledge graph from unstructured data dynamically and automatically. |
| Outcome: | The proposed framework outperforms multiple RAG implementations in both precision and recall, significantly enhancing user experience through improved retrieval accuracy. |
Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Existing approaches to text classification assume a fixed set of labels . however, in real-world applications, there exists an infinite label space for describing a given text . |
| Approach: | They propose two new methods that inject aspect-level understanding into pre-trained models at train time to improve zero-shot generalization. |
| Outcome: | The proposed methods improve zero-shot generalization on a set of challenging datasets. |