一種經濟高效且具有偏差意識的大型語言模型評估評分系統：Polyrating
Polyrating 是一種新的評分系統，旨在解決現有大型語言模型評估方法的局限性，透過多變量分析和偏差量化，提供更準確、經濟且可比較的模型性能評估。
LLM 평가를 위한 비용 효율적이고 편향 인식 평가 시스템: POLYRATING
POLYRATING은 기존 평가 시스템의 한계점을 해결하기 위해 고안된, 여러 작업에 걸쳐 LLM 성능을 보다 미묘하고 철저하게 분석할 수 있는 새롭고 비용 효율적인 평가 시스템입니다.
LLM評価のための費用対効果が高くバイアスを認識した評価システム：Polyratingの紹介
LLM評価のための費用対効果が高く、バイアスを認識した新しい評価システムであるPolyratingは、人間の評価におけるバイアスを定量化し、既存のベンチマークを活用してサンプル効率を向上させ、タスク間で比較可能なマルチレベルのリーダーボードを作成します。
Polyrating: A Multidimensional Rating System for LLM Evaluation that Detects Bias and Improves Sample Efficiency
Polyrating is a novel rating system for large language models (LLMs) that addresses limitations of traditional methods by incorporating bias detection, leveraging existing data to improve sample efficiency, and enabling multi-dimensional comparisons across tasks.
Training-Free Activation Sparsity for Efficient Inference in Large Language Models
TEAL, a training-free method for inducing activation sparsity in large language models, achieves significant inference speed-ups with minimal performance degradation by leveraging the inherent distributional properties of activations and specialized sparse kernels.
基於 ColBERT 資訊檢索和集成響應評分的語言模型問答系統
小型語言模型在特定領域的問答任務中面臨挑戰，本文提出了一種基於 ColBERT 資訊檢索和集成響應評分的問答系統，顯著提高了小型語言模型在電信領域問答任務中的效能。
소규모 언어 모델의 질의응답 성능 향상을 위한 ColBERT 검색 및 앙상블 응답 점수 매기기
본 논문에서는 ColBERT 검색 기반 검색 증강 생성(RAG) 파이프라인과 앙상블 응답 점수 매기기를 활용하여 소규모 언어 모델(Phi-2, Falcon-7B)의 통신 분야 질의응답 성능을 향상시키는 방법을 제시합니다.
小規模言語モデルを用いた電気通信分野の質問応答における、ColBERT検索とアンサンブル応答スコアリング
本稿では、専門性の高い電気通信分野の質問応答において、小規模言語モデルの性能を向上させるために、ColBERT検索に基づく情報検索と、応答スコアリングを組み合わせた手法を提案し、その有効性を示した。
Enhancing Small Language Models for Telecommunications Question Answering using ColBERT Retrieval and Ensemble Response Scoring
This paper proposes and evaluates novel question-answering systems based on fine-tuned Phi-2 and Falcon-7B models, achieving leading accuracy in a telecommunications question-answering challenge by leveraging ColBERT retrieval, technical abbreviation expansion, and ensemble response scoring.
利用多步驟流程總結冗長的法規文件
結合抽取式和生成式方法的多步驟架構，在處理冗長的法規文件摘要方面展現了潛力，但其有效性取決於模型架構和上下文長度等因素，並且需要仔細選擇合適的策略。
