Our research analyzes the in-context recall performance of various LLMs using the needle-in-a-haystack method. In this approach, a factoid (the "needle") is embedded within a block of filler text (the "haystack"), which the model is asked to retrieve. We assess the recall performance of...
C-ICL: Contrastive In-context Learning for Information Extraction Arxiv 2024-02 UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition ICLR 2024 GitHub Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering Arxiv 2024-01 Git...
In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations[openreview] Analyzing Task-Encoding Tokens in Large Language Models[arxiv 2401] How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learni...
招聘LLM应用落地(prompt+微调+RAG+小模型)的需求更多,人才供给也更多、更卷。 大模型热已经快1年半了,预训练的需求不再向1年前那么紧迫,但实际有从头训练或增量预训练的还是少数。如果候选人有实际的预训练经验、全量微调经验,甚至是成功的强化学习微调经验,估计不会再抓着这些八股死问,而是会交流训练心得、技术...
Context window size (number of tokens)– The context window, defined by the maximum number of tokens that can be input or output per prompt, is crucial in determining how much context the model can consider at a time (a token roughly translates to 0.75 words for English). Models ...
4.1 Prompt Tuning 4.1.1 Discrete Prompts 4.1.2 Continuous Prompts 4.2 Tuning-free Prompting 4.2.1 In-Context Learning 4.2.2 Chain-of-Thought 5. 基于LLM的面向任务的对话系统 5.1 基于流水线的TOD 5.1.1 Natural Language Understanding(NLU) - 自然语言理解 ...
根据以上分析,可以得出结论:在当前阶段,通过采用RAG方案,我们能够在成本仅为4%的条件下,取得比直接使用LLM的Long Context方案更出色的结果。RAG方案具有巨大的潜力! 参考 RAG vs. Context-Window in GPT-4: accuracy, cost, & latency Pressure Testing GPT-4-128K With Long Context Recall Claude 2.1 (200K To...
分类任务的评估指标:最常用的指标包括F1分数、精确度(Precision)和召回率(Recall),它们分别用于评估模型在正确分类代码片段或识别特定SE属性方面的能力。 例如,自动bug修复模型和基于Transformer的代码摘要模型的性能就是通过这些指标评估的。 推荐任务的评估指标:推荐任务常用的度量指标包括平均倒数排名(MRR)、Precision@k...
Beyond prompting, another effective way to steer an LLM is by providing knowledge as part of the prompt. This grounds the LLM on the provided context which is then used for in-context learning. This is known as retrieval-augmented generation (RAG). Practitioners have found RAG effective at pr...
这篇名为“LLM In-Context Recall is Prompt Dependent”的研究论文深入探讨了大型语言模型(LLMs)的关键评估以及它们从提示中检索特定信息的能力,这一过程被称为上下文回忆。这项研究特别值得注意,因为它采用了“大海捞针”的方... 内容导读 这篇名为“LLM In-Context Recall is Prompt Dependent”的研究论文深入...