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README.md

@@ -11,6 +11,7 @@ pipeline_tag: text-generation
 ---
 **Read this in other languages: [English](README_en.md), [中文](README.md).**
 
+* 2023.12.23更新：发布LongBench的passage_retrieval_en的评测结果
 * 2023.12.16更新：发布[论文(中文版)](https://cloud.tsinghua.edu.cn/d/5894ec4442e54a6aac96/)、[论文(英文版)](https://arxiv.org/abs/2312.11193)
 * 2023.12.14更新：发布经过微调的Qwen-14b-chat-yarn-32k，微调后的模型能适应32k长度（约4万汉字）的中英问答，相较于之前的通过位置插值得到的32k模型，几乎完全解决了多文档问答任务下召回率低（即 lost in middle 现象）的问题。
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@@ -31,6 +32,14 @@ pipeline_tag: text-generation
 | LongAlpaca-7b-32k-chinese-v2 | 0.12     |
 | CausalLM-14b                 | 0.086    |
 
+###  LongBench的passage_retrieval_en的评测结果
+| 模型                     | 得分 (acc) |
+|------------------------|----------|
+| **Qwen-14b-chat-yarn-32k** | **0.945**    |
+| Qwen-14b-chat          | 0.24    |
+| chatglm3-32k           | 0.815    |
+| gpt-3.5-turbo-16k      | 0.88     |
+
 Qwen-14b-chat-yarn-32k经过微调后，在多文档问答（或检索）任务上提升非常显著，大幅领先其他同规模的模型。
 
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README_en.md

@@ -11,6 +11,7 @@ pipeline_tag: text-generation
 ---
 **Read this in other languages: [English](README_en.md), [中文](README.md).**
 
+* Updated on December 23, 2023: Release the evaluation results of passage_retrieval_en in LongBench
 * Updated on December 16, 2023: Release [Paper](https://arxiv.org/abs/2312.11193)
 * Updated on December 14, 2023: We have released the Qwen-14b-chat-yarn-32k model, which has been fine-tuned to handle Chinese and English question-answering tasks with a length of up to 32k (approximately 40,000 Chinese characters). This model addresses the low recall issue in multi-document question-answering tasks (also known as the "lost in middle" phenomenon) that was present in the previous 32k model obtained through position interpolation. <br>
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@@ -20,15 +21,24 @@ pipeline_tag: text-generation
 # Evaluation results in LongBench
 ### Evaluation results for passage_retrieval_zh in LongBench
 
-| Models                               | Accuracy |
-|--------------------------------------|----------|
-| **Qwen-14b-chat-yarn-32k**           | **0.94** |
-| gpt-3.5-turbo-16k                    | 0.81     |
-| chatglm3-32k                         | 0.725    |
-| Qwen-14b-chat (use_dynamic_ntk=True) | 0.525    |
-| Qwen-14b-chat-32k-lora               | 0.34     |
-| LongAlpaca-7b-32k-chinese-v2         | 0.12     |
-| CausalLM-14b                         | 0.086    |
+| Models                       | Accuracy |
+|------------------------------|----------|
+| **Qwen-14b-chat-yarn-32k**   | **0.94** |
+| gpt-3.5-turbo-16k            | 0.81     |
+| chatglm3-32k                 | 0.725    |
+| Qwen-14b-chat                | 0.525    |
+| Qwen-14b-chat-32k-lora       | 0.34     |
+| LongAlpaca-7b-32k-chinese-v2 | 0.12     |
+| CausalLM-14b                 | 0.086    |
+
+###  Evaluation results for passage_retrieval_en in LongBench
+| Models                     | Accuracy |
+|------------------------|----------|
+| **Qwen-14b-chat-yarn-32k** | **0.945**    |
+| Qwen-14b-chat          | 0.24    |
+| chatglm3-32k           | 0.815    |
+| gpt-3.5-turbo-16k      | 0.88     |
+
 
 Qwen-14b-chat-yarn-32k has shown significant improvement in multi-document question-answering (or retrieval) tasks and outperforms other models of similar scale.
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