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COMPANY · ENTITY #4314

LLM-as-a-judge

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RESEARCH · 1 SOURCE · arXiv cs.AI

Pretrained ASR pseudo-labeling for noisy police audio uses LLM judging (arXiv:2609.30469v1)

This arXiv paper evaluates pseudo-labeling to adapt foundation ASR models (Whisper and Qwen3-ASR) to very noisy Broadcast Police Communication (BPC) corpora from Baltimore and Chicago. The authors find internal confidence metrics (log-probabilities, STAR scores) poorly separate good from bad pseudo-labels, propose an external LLM-as-a-judge filtering paradigm that discards contextually implausible transcripts and reduces WER of pseudo-labeled training sets, and introduce a cross-model pseudo-labeling approach where one model is fine-tuned on pseudo-labels from another.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

SIFT — Recursive Self-Improvement via Fast Tree-search (arXiv:2609.19526v1)

The paper introduces SIFT, a sample-efficient self-improvement framework for coding agents that uses an LLM-as-a-judge to perform pairwise comparisons of candidate code patches. Judge scores are aggregated with a regularized Bradley–Terry model to guide a lightweight disaggregated tree search, reserving expensive downstream task evaluations for the most promising nodes; the method reportedly outperforms prior tree-search self-evolution approaches on the full Polyglot benchmark while using far fewer CPU hours, wall-clock time, and API cost.

6.0