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cs.CL2026
Knowledge Graph-Assisted LLM Post-Training for Enhanced Legal Reasoning
Dezhao Song, Guglielmo Bonifazi, Frank Schilder +1
LLM post-training has primarily relied on large text corpora and human feedback, without capturing the structure of domain knowledge. This has caused models to struggle dealing wit…
cs.CL2025
Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses
Fangyi Yu, Nabeel Seedat, Dasha Herrmannova +2
Evaluating long-form answers in high-stakes domains such as law or medicine remains a fundamental challenge. Standard metrics like BLEU and ROUGE fail to capture semantic correctne…
cs.CL2025
Evaluating the Role of Verifiers in Test-Time Scaling for Legal Reasoning Tasks
Davide Romano, Jonathan Schwarz, Daniele Giofré
Test-time scaling (TTS) techniques can improve the performance of large language models (LLMs) at the expense of additional computation and latency. While TTS has proven effective…